CN115759328A

CN115759328A - Helicopter task planning method, system and equipment based on multi-objective optimization

Info

Publication number: CN115759328A
Application number: CN202211201357.7A
Authority: CN
Inventors: 韩维; 王毓麟; 苏析超; 袁培龙; 郁大照; 万兵; 郭放; 潘子双
Original assignee: Naval Aeronautical University
Current assignee: Naval Aeronautical University
Priority date: 2022-09-29
Filing date: 2022-09-29
Publication date: 2023-03-07

Abstract

The application relates to a helicopter mission planning method, a system and equipment based on multi-objective optimization, wherein the method comprises the following steps: acquiring known task parameters; calling the constructed amphibious helicopter training task planning model; calculating the population scale based on the task parameters and constructing an initial population; performing cross operation, mutation operation and local optimization search operation on the parent population, combining the generated offspring population and the parent population, and calculating a standardized solution set; generating a reference point set and a corresponding set of the Liji solution sets on the standardized solution set; distributing each individual in the standardized solution set to a radix-based solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each radix-based solution set based on a scaled sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation of population; and repeating population evolution iteration until the maximum iteration times are reached, and outputting training task planning scheme data of the amphibious helicopter. Efficient and rapid planning of training tasks of the amphibious helicopter.

Description

Helicopter task planning method, system and equipment based on multi-objective optimization

Technical Field

The invention belongs to the technical field of mission planning, and relates to a helicopter mission planning method, a system and equipment based on multi-objective optimization.

Background

In an amphibious task, a helicopter taking a marine forward base as a take-off and landing platform (LHD) can cross obstacles on a water beach by virtue of the characteristics of flexible deployment and quick concealment, and task resources are delivered at a shallow depth and a deep depth of a landing field to form local advantages at the same time, so that the sudden and quick decision of the amphibious task is achieved. However, the helicopter has the natural defects of complex guarantee, low play intensity, limited carrying capacity, easy interference and the like, so that the helicopter is more limited in use and has higher task risk. Therefore, the mission of the amphibious helicopter needs to be finely planned so as to fully exert the advantages thereof and simultaneously reduce the loss as much as possible.

Currently, mission planning research on aircraft grouping mainly focuses on the field of UAV (Unmanned Aerial Vehicle) cluster action planning, which can be roughly divided into two parts, namely mission allocation research and route planning research. The content of the task allocation research mainly comprises allocation decision of tasks among different resources and platforms and optimal scheduling of task execution sequence, and the current mature models in the field comprise a Traveling Salesman Problem (TSP) model, a Vehicle Routing Problem (VRP) model and a mixed integer linear programming problem (MILP) model. Traditional training task planning models generally aim to achieve the maximum results, sustain the minimum losses, complete tasks in the shortest time, and make the most of human resources.

In reality most training task planning problems are Multi-objective Optimization problems (MOP). At the present stage, a Multi-object evolution Algorithm (MOEA) is proved to be the most effective way to solve the MOP, and can be roughly divided into three categories, namely, based on Pareto dominance, based on indexes and based on decomposition, according to the solution idea. However, the traditional training mission planning method cannot directly solve the technical problem of rapid planning of training missions of the amphibious helicopter in reality.

Disclosure of Invention

Aiming at the problems in the traditional method, the invention provides a multi-objective optimization-based helicopter task planning method, a multi-objective optimization-based helicopter task planning system and computer equipment, which can realize the rapid planning of training tasks of an amphibious helicopter efficiently.

In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:

on one hand, the method for planning the task of the helicopter based on multi-objective optimization is provided, and comprises the following steps:

acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave frequency of helicopter operation, the solution space dimensionality, the simplex partition parameter and the maximum iteration frequency;

calling the constructed amphibious helicopter training task planning model; constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and an optimization objective function of the amphibious helicopter training mission planning model comprises a time-consuming objective function of a minimum landing phase, a minimum manpower loss objective function and a ground threat objective function of the minimum helicopter;

calculating the population scale of the amphibious helicopter training task planning model based on the task parameters and constructing an initial population;

performing cross operation, mutation operation and local optimization search operation on the parent population to generate a child population;

combining the generated child population and the parent population to obtain a combined population, updating an IDEAL point and an NADIR point according to elements in a solution set corresponding to the combined population, and calculating to obtain a standardized solution set according to the IDEAL point and the NADIR point;

generating a reference point set and a corresponding set of the Liji solution sets on the standardized solution set;

distributing each individual in the standardized solution set to a radix-based solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each radix-based solution set based on a scaled sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation of population;

and returning to the step of performing cross operation, mutation operation and local optimization search operation on the parent population to generate the offspring population, and outputting training task planning scheme data of the amphibious helicopter until the iteration number of the population reaches the maximum iteration number.

In another aspect, a system for planning a mission of a helicopter based on multi-objective optimization is further provided, including:

the parameter acquisition module is used for acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave times of helicopter operation, solution space dimensionality, simplex segmentation parameters and the maximum iteration times;

the model calling module is used for calling the constructed amphibious helicopter training task planning model; constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and an optimization objective function of the amphibious helicopter training mission planning model comprises a time-consuming objective function of a minimized landing phase, a minimized manpower loss objective function and a target function of ground threat of a minimized helicopter;

the population construction module is used for calculating the population scale of the amphibious helicopter training mission planning model based on the mission parameters and constructing an initial population;

the child generation module is used for performing cross operation, mutation operation and local optimization search operation on the parent population to generate a child population;

the merging calculation module is used for merging the generated child population and the parent population to obtain a merged population, updating an IDEAL point and an NADIR point according to elements in a solution set corresponding to the merged population, and calculating to obtain a standardized solution set according to the IDEAL point and the NADIR point;

the set generating module is used for generating a reference point set and a set of corresponding interest-base solution sets on the standardized solution set;

the new population module is used for allocating each individual in the standardized solution set to a sharp root solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each sharp root solution set based on a scaled sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation population;

and the iteration control module is used for outputting the training task planning scheme data of the amphibious helicopter when the population iteration times reach the maximum iteration times.

In still another aspect, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the helicopter mission planning method based on multiobjective optimization when executing the computer program.

In yet another aspect, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method for helicopter mission planning based on multiobjective optimization.

One of the above technical solutions has the following advantages and beneficial effects:

according to the helicopter mission planning method, system and equipment based on multi-objective optimization, the main mission mode and characteristics of the helicopter in the amphibious landing operation are extracted and analyzed, the amphibious helicopter training mission planning model is provided on the basis of reasonable assumption of a real landing training scene and is expressed as a multi-objective optimization model, and the aim is to minimize time consumption of the landing operation, personal casualties of own parties and threat of sky-proof fire of own helicopter to blue parties. The training mission planning model of the amphibious helicopter considers the specific practical constraints of two aspects of training resources, training and guaranteeing the full-link mission stage time sequence, which can be used by multiple platforms. A new initial solution generation, crossing, variation and local optimization method is provided on the aspect of a population updating iterative mechanism, the search efficiency of a code definition space is improved, and the optimization efficiency and the population diversity maintenance are well represented, so that the aim of quickly planning training tasks of the high-efficiency and real amphibious helicopter is fulfilled.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram of a multi-objective optimization-based helicopter mission planning method in one embodiment;

FIG. 2 is a schematic illustration of a flight mission and deck preparation phase of a helicopter formation in one embodiment;

FIG. 3 is a schematic flow diagram of an iterative optimization solution to a model in one embodiment;

FIG. 4 is a schematic diagram of an example of chromosome coding in one embodiment;

FIG. 5 is a schematic illustration of an example helicopter launch plan in one embodiment;

FIG. 6 is a schematic diagram of a process for interleaving one chromosome code with another chromosome code in one embodiment;

FIG. 7 is a schematic diagram of the modular components of a multi-objective optimization-based helicopter mission planning system in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

It should be appreciated that reference throughout this application to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.

One skilled in the art will appreciate that the embodiments described herein may be combined with other embodiments. The term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.

Training task analysis: the helicopter marshalling composed of the general helicopter and the special helicopter can execute various tasks including personnel, equipment and material vertical delivery, near-distance air support, low-altitude air control right capture, sea surface target striking, anti-submarine mine sweeping and the like in an amphibious landing task. In the amphibious task process, a helicopter taking off from an LHD can land vertically in the depth and depth of a target coast, a delivery machine descends manpower to cooperate with a plane to land, landing team members are assisted to rapidly advance on the target coast, or assault is carried out on target side wings or rear important points, so that a local task target is achieved. At present, the helicopter is limited by the dynamic strength and the load and can only deliver light duty units in a small scale, so that the vertical landing unit mainly bears the cooperative task of matching with the plane landing unit in a large-scale amphibious landing task.

According to the analysis, the vertical landing task can be abstracted into the distribution of the general helicopter load (airplane landing manpower) of the offshore platform to different points of the target, and the selection and the calculation of factors such as the impact point, the impact opportunity, the airplane landing field and the helicopter route control point are specifically included. The main body for executing the vertical landing task is a general helicopter for the offshore platform, and the helicopter also comprises a special helicopter for the offshore platform, which is used for accompanying navigation, reconnaissance and shielding.

In the implementation process of the vertical landing task, consideration needs to be given to influences on vertical landing task efficiency such as cooperation of the LHD and a ship aircraft of the helicopter, intra-formation cooperation of the helicopter and the aircraft landing manpower, inter-formation cooperation between task formations of different targets in different wave times and the same wave time, inter-work-type cooperation between a vertical landing unit and a plane landing unit, and the like, and specifically includes influences on helicopter takeoff efficiency by platform guarantee capability, influences on manpower delivery efficiency by load, speed and flight of the general helicopter, influences on helicopter formation flight safety by operating field information control right, air control right and target air defense suppression effect, and the like.

By virtue of its characteristic flight characteristics, a helicopter dedicated to offshore platforms can achieve a flight altitude far below that of fixed-wing aircraft and a maneuvering speed far beyond that of wheeled or tracked equipment. Therefore, in a near-distance air support task implemented mainly by the offshore platform special helicopter, the special helicopter marshalling taking off from the LHD deck or standing by in the front airspace can be concealed and quickly maneuvered to a target airspace by using ultra-low-altitude flight, and accurate target assault is implemented on important targets such as target vehicles, radar stations, target places, communication hubs, front sentries, simple works, target sites, surface ships, ground units and the like, so that the method has irreplaceable important function.

Similar to the vertical landing task, the near-distance air support task can be abstractly understood as the distribution of the helicopter load special for the offshore platform to different targets, and specifically comprises the selection and calculation of the task target, the attack opportunity, the helicopter air route control point and other elements. The near-distance air support task is executed by a helicopter special for an offshore platform, and may also comprise an unmanned aerial vehicle for providing operation field situation reconnaissance and the like. In the task implementation process, the influence of factors such as ship surface guarantee capability, special helicopter load, speed, range, air defense targets and operating field situation on the attack efficiency of the helicopter targets also needs to be considered.

In one embodiment, as shown in fig. 1, the present application provides a method for helicopter mission planning based on multi-objective optimization, comprising steps S11 to S18:

s11, acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave times of helicopter operation, the solution space dimensionality, the simplex segmentation parameters and the maximum iteration times.

It is understood that before the optimization calculation is started, each preset relevant task coefficient, such as but not limited to the total number of platforms carrying the takeoff and landing platform of the helicopter and the total number of wave motions of the helicopter, and relevant known parameters of the optimization calculation, such as a solution space dimension, a simplex partition parameter, a maximum iteration number, and the like, may be obtained.

S12, calling the constructed amphibious helicopter for training task planning model; the constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and the optimization objective functions of the amphibious helicopter training mission planning model comprise a time-consuming objective function of a minimum landing phase, a minimum manpower loss objective function and a ground threat objective function of a minimum helicopter.

It will be appreciated that amphibious federated landing is the most complex form of action, and that training mission planning models contain too many elements, necessitating appropriate specification assumptions to make the model relatively simple. For convenience of description, the attacking party of the attacking and defending parties can be called the red party, and the defending party can be called the blue party. The model assumes 1 to be: all training tasks in the login action of the red party are completed by a plane login team, a vertical login team and a helicopter together, wherein the training tasks of the plane login team are formulated by plan before the action, and the helicopter mainly undertakes the vertical login task and a close distance support task.

Model hypothesis 2 is: the vertical landing tasks are completed by the general helicopters and the special helicopters together, the types and the number of the vertical landing teams delivered to each target of the blue party and the required number of the two types of helicopters are formulated in a participation plan before action, the short-distance support tasks are completed by the special helicopters only, and the grouping of each short-distance support task is composed of a fixed number of special helicopters.

The model assumes 3 as: the helicopter can not influence the training task executed by the helicopter due to failure or damage, the LHD can not be attacked, and the helicopter can stably provide sufficient oil and ammunition supply and various mechanical service guarantees for the helicopter.

Model assumption 4 is: the situation that the LHD is close to the landing beach (for example, 50 km) is selected for research, the time for executing the task of ascending and descending the helicopter mission group is short, so that the helicopter is supposed to take double-wave circulation to move, and the helicopter group with the same mission moves at the same or near-wave as much as possible.

The model assumes 5 as: each helicopter is assumed to have the same ship surface allocation and transportation, service guarantee, departure and departure, formation flight (between LHD and landing area) and arrival and landing time, the helicopter is assumed to fly at a constant speed at ultra-low altitude in the landing area, the flight time depends on the flight path length of the helicopter in the landing area, and in addition, each vertical landing task grouping and each close-distance support task grouping are assumed to have the same and fixed landing time and ground hitting time respectively.

Model dependent variable definition: the relevant variable symbols in the model are given in table 1.

TABLE 1

The model problem constraint comprises two categories of training resource constraint and task stage time sequence constraint, wherein the training resource constraint comprises the following steps:

(1) And (3) restricting the number of vertical login teams: according to the model assumption 4, in order to deliver more vertical landing teams simultaneously in one wave, the vertical landing teams should be deployed on different LHDs in a scattered manner, and since the total number of vertical landing teams that can be input by the red party in the landing movement is limited, the number of vertical landing tasks that each LHD can support must also meet the limit of the number of vertical landing teams loaded by the LHD, specifically,

the following constraints should be satisfied:

meanwhile, the total number of all vertical login teams delivered by all vertical login task plans in one login movement also meets the following constraint:

(2) The number of helicopters is restricted: according to model hypothesis 2, the sum of the helicopters required for the task formation of helicopters with adjacent wave cycles on a certain LHD is limited by the total number of helicopters loaded by the LHD:

the number of dedicated helicopters required for each individual training mission in equation (3)

And universal helicopterNumber of machines

The ratio between them is determined by the task type c, when c =2 the helicopter group performs the close range support task, the task group is only composed of special helicopters, therefore the number of general helicopters

When c =1, the helicopter group performs a vertical landing task, and the task group is composed of a general helicopter and a special helicopter for which the accompanying shield is provided, and the number of the special helicopters is generally

Number of general helicopters should not be less than

1/2, it is therefore assumed in this application that:

in the above formula (4)

Indicating the number of helicopters required to round up x, and in addition, for each vertical landing mission

Depending on the number of vertical landing teams of each type required to carry out an air-assault on the pth node of the blue in a staff-before-red-action conspiracy plan

(3) LHD guarantees resource constraints: according to model assumption 4, limited to the number of takeoff positions on each LHD flight deck, the number of helicopters grouped by the same wave-time takeoff helicopter mission should satisfy the following formula (6):

in addition, the helicopter consists assigned to the same training mission should behave as much as possible in the same or in adjacent wave groups, and therefore in the formula

The constraint should be satisfied:

in the above formula (7), n _w And (3) representing the minimum wave number required by all the helicopter marshalling in the c-th training task distribution of the target p, namely rounding up of the quotient of the sum of the two types of helicopters required by the task marshalling divided by all the takeoff positions of all the LHDs in the red.

Task phase timing constraints include:

(1) Task phase timing constraints based on execution order: a complete helicopter training task comprises 9 parts, namely a ship surface dispatching stage, a service support stage, a take-off and departure stage, a formation flight stage (cruising) from an LHD to a coastline, a formation flight stage (ultra-low altitude penetration) from the coastline to a target node, a task execution stage, a formation flight stage (ultra-low altitude penetration) from the target node to the coastline, a formation flight (cruising) from the coastline to the LHD, an approach landing stage and the like, wherein the following constraints are satisfied between the starting time and the ending time of each stage in each independent helicopter training task:

in the formula (8), the reaction mixture is,

and

satisfies the following relationship:

in formula (9)

And the consumption time of the kth sub-stage of executing the c-type training task on the p-th node target of the landing field on the first LHD of the red party in the w-th wave is shown. Wherein the consumed time of the ship surface allocation and transportation stage, the aircraft service support stage, the takeoff and departure stage and the approach and landing stage

And

for a hypothetical fixed value, the task execution phase is time consuming

Helicopter formation flight time determined by task type

And

the optimal planned route length is divided by the helicopter formation flying speed.

It is noted that although each helicopter training task in the present application contains 9 sub-phases, not all helicopter consists assigned to training tasks will go through these 9 phases in their entirety. For example, in the 1st stage ship surface transfer, if the w and w +2 wave times of a certain LHD cycle outAll task marshalling need the same total number of various helicopters, then the helicopters that wave out can directly carry out the service support on taking off position after landing, then directly lift off and form the task marshalling of w +2 wave times and carry out the training task. Thus in equation (9)

Satisfying the following equation.

T in formula (10) ₁ Indicating that the assumed ship surface is time-consuming to ship.

(2) Task stage timing constraints based on LHD deck scheduling: due to the limited space of the LHD flight deck, the flight service support of the helicopter on the LHD usually occupies a takeoff position, so that a helicopter team ready for entering and landing must wait for the emptying of the LHD flight deck (namely, the helicopter takes off next wave time) to land. Therefore, for a helicopter formation traveling on adjacent 2 waves at the same LHD, the take-off and landing times must satisfy the following constraints:

in the formula (11), the reaction mixture is,

representing landing moments of helicopter consists performing class c training tasks on the p-th node target of the landing field on the w' -th wave of the first LHD on the redside,

the departure time of a helicopter consist performing the same task on the w "th wave run on the same LHD is indicated.

As shown in fig. 2, if it is assumed that the helicopter mission groups running on the same wave on all LHDs in the red have to take off simultaneously, after all the helicopter groups on the w wave fall, the helicopter group No. (1) with shorter preparation time for performing service duty on the takeoff position waits for the helicopter group No. (2) requiring deck adjustment to replace the helicopters to take off together after the completion of the service duty. After the helicopters (1) and (2) are completely marshalled to be lifted off, the helicopters (3) and (4) which return to the ground after the task is executed for the w-1 wave are marshalled to land on the deck after being emptied.

Model optimization objective: on one hand, because the manpower that can be accommodated by the beach head is limited, the red land team can only put into the field wave by wave, and in order to prevent the counter shock that the blue party possibly initiates at any time, the first wave land team must quickly occupy and consolidate the landing field, and the subsequent team can safely and quickly land; on the other hand, the blue party, who faces firm work, is at a disadvantage in terms of firepower and protection due to the lack of re-equipment of the red party first wave upland team. Therefore, the Hongfang team must reasonably plan the manpower application and reduce the self casualties as much as possible on the premise of ensuring that the scheduled training tasks are completed on time. By combining the analysis, the time consumed by training in the landing stage of the landing field, the labor consumption, the ground threat of the helicopter and the like opened by the Hongdang are selected as the optimization targets of the model.

(1) Minimizing the time consumed in the login phase: in the amphibious landing action, a defense party usually organizes power and manpower to perform counter-impact when an attacking party is unstable, whether the defense party quickly occupies the land and effectively consolidates the land before the defense party initiates the counter-impact is an important factor for determining whether a landing action is successful or not. Therefore, the total time consumption of all training teams of the red party for completing all established tasks of the stage of opening a landing field is selected as a landing stage time consumption objective function by the model:

z ³ ＝T _fin -T ₀ (12)

t in formula (12) _fin The method comprises the following steps of (1) representing the end time of a login stage, namely the time when a first wave upland team on the red side finishes opening and consolidating various tasks required by a beach login field; t is a unit of ₀ And initiating the corresponding login action, namely the starting takeoff moment of the first wave-time helicopter in the red party.

(2) Minimizing manpower loss: the manpower loss in the red square comprises plane landing manpower loss and vertical landing manpower loss, and the manpower loss objective function can be specifically expressed as:

in the formula (13), the reaction mixture is,

represents the manpower loss of all training teams of the red party training at the p-th blue party node in unit time at the time point t,

represents the starting moment of the countermeasure at the p-th blue-party node, T ^p Expressed in minutes, at challenge duration. The application utilizes the existing Lanchester equation to calculate the manpower loss of the red and blue.

(3) Minimizing ground threat to helicopters: in order to avoid the model from being too complex, the influence of the damaged red helicopter on two training tasks, namely manpower marshalling and subsequent available helicopters on the LHD is not considered, and only the blue anti-aircraft threat accumulation borne by the helicopter in the flight and navigation process is used for quantitatively representing the loss of the helicopter:

in formula (14), k ^Scale The scale factor is expressed in terms of a scale factor,

and

and E (x, y, t) represents the sum of all ground threat values of each helicopter in the red side and each helicopter in the blue side in unit time at the coordinate (x, y) at the t moment. The present application simplifies the computation of E (x, y, t) appropriately, considering only the blue square perThe air defense fire coefficient of each node, the distance between the red square helicopter and the node and the shielding effect of the terrain on the blue square air defense fire, and E (x, y, t) can be calculated according to a formula (15).

Eta in formula (15) _SAM A boolean variable indicating whether the node p and the coordinates (x, y) are occluded by the terrain, which is 1 when not occluded and 0 when occluded;

representing the air defense fire coefficient at the blue square node p; d (x, y, p) represents a planar distance between the node p and the coordinates (x, y). And (3) the position (x, y) of each helicopter in the red square at the time t is obtained by solving a path plan based on dijkstra (Dijkstra) algorithm in the later decoding operation.

S13, calculating the population scale of the amphibious helicopter training task planning model based on the task parameters and constructing an initial population;

s14, performing cross operation, mutation operation and local optimization search operation on the parent population to generate a child population;

s15, merging the generated offspring population and the parent population to obtain a merged population, updating IDEAL points and NADIR points according to elements in a solution set corresponding to the merged population, and calculating according to the IDEAL points and the NADIR points to obtain a standardized solution set;

s16, generating a reference point set and a corresponding set of a Liji solution set on the standardized solution set;

s17, distributing each individual in the standardized solution set to a radix-based solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each radix-based solution set based on a scaled sorting method, and selecting individuals corresponding to elements with the top rank to form a new generation of population;

and S18, returning to the step of executing cross operation, mutation operation and local optimization searching operation on the parent population to generate the offspring population, and outputting training task planning scheme data of the amphibious helicopter until the iteration number of the population reaches the maximum iteration number.

It can be understood that the traditional planning method is inefficient in solving the problem that the objective function and the constraint are non-linear, and is sensitive to the weight and the optimization order. With the continuous development of the multi-objective optimization problem in the actualization and complication directions, the multi-objective evolutionary algorithm becomes a main means for solving the problem.

According to the helicopter mission planning method based on multi-objective optimization, the main mission modes and characteristics of the helicopter in the amphibious landing operation are extracted and analyzed, the amphibious helicopter training mission planning model is provided on the basis of reasonably assuming the real landing training scene and is expressed as a multi-objective optimization model, and the aim is to minimize the time consumption of the landing operation, personal casualties of own parties and the threat of blue party airproof fire of own helicopter. The amphibious helicopter training task planning model considers specific practical constraints of multiple platforms which can be used as training resources, training and guaranteeing the whole-link task stage time sequence. A new initial solution generation, crossing, variation and local optimization method is provided on the aspect of a population updating iteration mechanism, the search efficiency of a code definition space is improved, and the optimization efficiency and the population diversity maintenance are well expressed, so that the aim of quickly planning training tasks of the high-efficiency and real-life amphibious helicopter is fulfilled.

In order to express the sequential relation between each training task as simply and clearly as possible, the application adopts a discrete task list coding form similar to VRP. According to model constraint 2, when the total number of LHDs and the total number of oscillations are given, the total number of helicopters required by all training tasks in the chromosome codes is also determined, so that the training tasks of different numbers of helicopters required in the amphibious helicopter training task planning model can lead to different lengths of the chromosome codes. In addition, according to the analysis, all training tasks are difficult to be contained in the corresponding chromosome codes of any decision variables of the amphibious helicopter training task planning model, so that the traditional genetic operator is difficult to realize the cross operation of chromosomes with different lengths, and the problem definition domain is difficult to be effectively searched by virtue of the chromosomes only containing limited coding information. Aiming at the problems, the method improves the population updating iterative mechanism of the evolutionary algorithm, redesigns the cross operation and variation operation methods suitable for codes with different lengths, and innovatively provides an initial solution generation method and a local search method to improve the optimization efficiency of algorithm processing.

The known parameters include the solution space dimension (denoted as m), the simplex partitioning parameter (denoted as H), and the maximum number of iterations of the algorithm (denoted as gIter). First, the population size | P is calculated according to equation (16) _g |：

Then, an Initial Population P is constructed by an Initial Population Generation Method (Initial Population Generation Method based on transformation 1st-Class Neighborhods, IPGM-T1N) based on the search of Class 1 neighborhood ₀ (ii) a Second, for the parent population P _g Performing crossover, mutation and local optimization operations to generate a progeny population o ″ _g+1 (ii) a Thirdly, the generated o ″) _g+1 And parent population P _g Merging to obtain a merged population P' _g+1 According to the combined population P' _g+1 Corresponding solution set Z' _g+1 Element update z in (1) _Ideal Parameter and z _Nadir Parameters and further calculating according to formula (17) to obtain a normalized solution set

In the above formula (17), m represents a solution space dimension,

and

respectively representing original solution sets Z andstandardized solution set

Of (a). The fourth step is that

On the basis, a reference point set Lambda and a corresponding set omega of a litykoff solution set are obtained by using the existing reference point generation method based on population distribution, wherein Lambda = { Lambda = (lambda) } ₁ ,…,λ _n },Ω＝{ω ₁ ,…,ω _n },n＝|P _g L. the method is used for the preparation of the medicament. Fifthly, the standardized solution is collected by using the existing clustering method based on the Inner Angle Measure (IAM)

Each of which

Assign to a Riji solution set omega _λ Belongs to omega, and then based on the existing scaling-based sequencing Method (SSM), the Method is applied to each omega _λ The non-dominant sorting operation is carried out, and the individuals corresponding to the elements with the top rank are selected to form a new generation population P _g+1 . Finally, repeating the second step to the fifth step until the iteration number of the population reaches the maximum iteration number g _Iter And outputting the finally optimized amphibious helicopter training task planning scheme data.

In one embodiment, several main component relationships of the proposed model optimization solution algorithm are shown in FIG. 3. The chromosome coding mode adopted in the population iteration process is as follows: decomposing all training tasks meeting the condition of setting the number of helicopter marshalls into a single task list with the number of helicopter marshalls equal to 1 and adopting discrete double-row chromosome coding; adding a gap gene in the chromosome coding of the task list,

specifically, as shown in FIG. 4, all conditions that satisfy the set helicopter consist number (i.e., meet the set helicopter consist number condition) will be met

) The training task is decomposed into the number of helicopter marshalling

Takes a discrete two-line chromosomal coding:

σ in formula (18) _i Denotes a chromosome-encoding gene, and the first row c and the second row p have the same meanings as in table 1;

represents the length of the chromosome, i.e., the total number of helicopter training missions.

According to the constraint of the formula (3), if the total number of the helicopters of a certain type on the LHD is not enough to guarantee the continuous calling of training tasks of two adjacent waves, limited helicopter resources must be allocated to a certain wave, and the other wave can only schedule tasks requiring other helicopters or tasks which are not scheduled. Therefore, it is necessary to encode

Adding empty genes to indicate the idle state of some takeoff sites of a certain LHD in a certain wave, as used in this application

To represent this idle state and assume that the number of idle takeoff positions is the greatest common factor of the number of helicopters required for all training missions.

According to model hypothesis 2, the neighborhood where each gene in the chromosome code is located is classified into 2 types: genes corresponding to vertical landing missions (hereinafter referred to as class 1 training missions) with variable helicopter number are required to be located in class 1 neighborhoods; genes corresponding to close range support tasks (hereinafter referred to as class 2 training tasks) requiring a fixed number of helicopters are located in class 2 neighborhoods. It is well known that the variation in chromosome length is caused by the variation in class 1 training tasks in the code.

The chromosome decoding mode adopted in the population iteration process is as follows: converting chromosome codes of the task list into an action list representing the action plan of each helicopter; obtaining a list of initiating moments of vertical login tasks and close-range support tasks of each target through the going-out list; according to the play list, the launch time list and the predetermined training plan of the plane landing team, the improved Lankaster model is used for calculating the variation of the force of each target node of the landing field along with the time to obtain a landing field node state vector, the progress direction of the landing action is deduced through the landing field state transfer model, and the achievement situation of the training purpose and the training task efficiency index set are determined.

It will be appreciated that with respect to the algorithmic decoding portion, the input parameters for a particular decoding operation are a discrete task list

The decoding process is roughly as follows: first, the discrete coding of the sequence characterizing the training task

Conversion into a play list characterizing the play plan of each helicopter

By passing

The obtained list of the initiation moments of the vertical login task and the close range support task of the target of the red party to the blue party

Combining with a predetermined training plan of a plane landing team, calculating the change situation of each target node of a landing field against manpower of two parties along with time through a Lanchester model improved in the field, further obtaining a landing field node state vector delta, deducing the progress direction of a landing action through a landing field state transfer model, and finally determining whether a red party can finally achieve the action purpose and performing a training taskSet of performance indicators

In one embodiment, the process of converting to an action list further comprises the following steps:

calculating the number of all vertical landing teams delivered by each vertical landing task in the chromosome code of the task list and the number of various helicopters required by each vertical landing task and the close distance support task;

selecting 1 class training tasks meeting training resource constraint in a task list according to the number of all vertical landing teams and the number of various helicopters, assigning values to elements in an action list according to a set assignment formula, and adding the executed tasks to an executed task list according to the original list sequence; the class 1 training task is a vertical login task;

after all the class-1 training tasks meeting the training resource constraint are inserted into the action list, selecting class-2 training tasks meeting the training resource constraint in the task list according to the number of all vertical landing squadrons and the number of all helicopters, and correspondingly updating the action list and the executed task list; the class 2 training task is a close range support task

Specifically, in

And

the solving stage of (2): first, the chromosomal coding of the task list is computed

The number u of all vertical Landing Units (ALUs) delivered by each vertical Landing task _i And the number h of helicopters of each type required for each vertical landing mission and for short-range support missions _i . Second step according to u _i And h _i Selecting a training task list

The class 1 training task satisfying the constraints of the formulas (1) to (7) is given by the set assignment formula (19)

Assigning values to the elements in (1) and adding the executed tasks to the list in the original list order

In (1). Thirdly, all class 1 training tasks meeting the constraint are inserted

Then, the search is performed according to the same principle as the second step of this paragraph

Class 2 training tasks satisfying the constraints and updating accordingly

And

assuming 3 LHDs in the red and sufficient helicopters, 3 waves of movement are accumulated, the chromosome coding in FIG. 4

Transcribable to a helicopter out-of-flight schedule as shown in FIG. 5

Is a single row of the number w of lines,

a matrix of the columns is formed,

represents the task executed by the helicopter taking off from the jth takeoff position of the ith LHD in the w wave number, and the value of the task is as followsEquation (19) shows:

further, the list of the initiation moments

The solving process of (2) may include the following processes:

according to the task targets of all training tasks in the action list and the distribution condition of target air defense nodes in a landing field, marking out a route set with the minimum risk from the helicopter to each task target by utilizing Dijkstra's law;

and solving by using the action list and the airline set to obtain an initiating moment list of the task execution stage of each training task.

Specifically, in

The solving stage of (2): first, according to the helicopter's plan of action

Target p of each training task _i And planning the helicopter to each task target p by utilizing Dijkstra (Dijkstra) algorithm in combination with the known or assumed distribution condition of target air defense fire in landing fields _i Of the least risky course set

Secondly, solving a list of starting moments (for a vertical landing task, the moment when a vertical landing squad starts to land, and for a close-distance support task, the moment when a special helicopter starts to attack on the ground) of task execution stages of each training task

Further, the process of determining the training task performance index set may specifically include the following processing:

setting the value of a corresponding landing field node state vector to be zero at the moment of initiating a landing action;

solving the bearing risk of each helicopter in the marshalling task flight process based on the setting;

according to the initiation time list and a predetermined training plan of a plane landing team, finding out landing site nodes which are confronted at each simulation time, calculating the labor loss and time consumption of each landing site node for confronting two parties, updating corresponding landing site node state vectors according to landing site situation transfer conditions, and performing numerical integration operation on the actual force vectors of the two parties at each target node by setting a simulation step length until the landing site node state vectors meet a predetermined target of a landing action opening-up site stage or a landing site failure training target is determined;

and (4) counting the total consumption time of the stage of opening the landing field, the accumulated manpower loss of the vertical landing sub-team and the plane landing team and the bearing risk of all helicopters executing the training tasks to form a training task efficiency index set.

Specifically, in

The solving stage of (2): first, assume that T is initiated at login action ₀ Time of day to

Landing site node status vector Δ (p) =0. Secondly, solving the bearing risk of each helicopter in the red square in the flying process of the marshalling task; thirdly, initiating time according to the training task of each helicopter

Combining with the predetermined training plan of the plane login team, finding out the login field node in each simulation moment, and calculating the manpower consumption and the time consumption (i.e. time consumption) of each node in the confrontation by using formulas (12) - (15) to t _step The actual force vectors of the manpower of the red and blue parties at each target node are carried out for setting the simulation step lengthThe numerical integration operation is carried out until the predetermined target of the red party at the stage of opening the landing place for landing actions is met or the red party determines that the action target can not be achieved. Finally, calculating to obtain the total consumption time z of the stage of opening up the landing field ¹ Cumulative human loss z for a laterove and a level rove ² And all the helicopters performing the training task are subjected to a risk z ³ Forming a target vector

In an embodiment, the process of constructing the initial population in step S13 may specifically include the following processing steps:

searching all 1-class training task combinations meeting the vertical login squad number constraint to form a 1-class training task set;

determining the total number of tasks contained in the target chromosome according to the number constraint of the helicopters; the target chromosome is a chromosome containing elements in the class 1 training task set;

constructing an arrangement set which comprises all arrangement forms of the corresponding elements in the class 1 training task set in the chromosome with the length being the total task number based on the total task number and the task number contained in the corresponding elements in the class 1 training task set;

for each element in the permutation set, taking the residual space in the chromosome corresponding to the element as a class 2 neighborhood and randomly assigning values to obtain a permutation integrity code; the class 2 neighborhood is the neighborhood where the corresponding gene of the class 2 training task is located;

carrying out chromosome decoding on the permutation complete code to obtain a corresponding permutation target value;

if the permutation target value is a feasible solution, combining the permutation complete code and the corresponding permutation target value as an individual in the initial population;

and returning to the step of determining the total task number contained in the target chromosome according to the number constraint of the helicopters until each task contained in each element in the ergodic arrangement set, and outputting an initial population containing an initial coding set and an initial solution set.

It can be understood that, since information including all Class 1 training tasks cannot be guaranteed in a single chromosome code, and different Class 1 training tasks in each chromosome directly determine the difference of the code length, in order to make an algorithm perform a full search on Class 1 Neighborhoods in a feasible region, and to make each chromosome code in an Initial Population include as many training tasks as possible while satisfying the constraints, an Initial Population Generation Method (Initial Population Generation Method on transforming 1st-Class neighbor nodes, IPGM-T1N) based on the search Class 1 Neighborhoods is designed, a feasible code set including all different Class 1 training task permutation combinations (i.e., all possible lengths) is generated and used as the Initial Population, and the diversity of the Initial Population is better guaranteed.

Specifically, the input is the population size | P |, which is defined as | P in equation (16) _g The same. Firstly, searching all 1-class training task combinations meeting the total number constraint of vertical landing teams, namely formula (2), and forming a 1-class training task set C ^S . Second step, for C ^S The ith element C ^S (i) First, the inclusion C is determined according to the constraint of equation (3) ^S (i) Target chromosome of (2)

Total number of tasks involved

(i.e., the chromosome coding length) and then based on

And C ^S (i) Number of tasks | C in ^S (i) The | construction includes C ^S (i) In a length of

Of all permutations in the chromosome

According to the definition of the arrangement,

the number of the medium elements can be calculated by the formula (20):

the third step, for

Each element of (1)

Firstly, the residual space in the corresponding chromosome is taken as a 2-type neighborhood, and random assignment is carried out to obtain the permutation complete code

Then, the decoding algorithm in the former is called to obtain the corresponding arrangement target value

If it is

To be a feasible solution, then

And

combined as initial population P ₀ Of the subject. Finally, the second step and the third step in the embodiment are repeated until i = | C ^S (i) Until | the output contains the initial encoding set C ₀ And an initial solution set Z ₀ Initial population P of ₀ 。

In an embodiment, the process of performing the crossover operation on the parent population in step S13 may specifically include the following steps:

carrying out length transformation on the two chromosome codes to be crossed, and determining the position of a gene point which can be crossed and can carry out double-point crossing operation in the two chromosome codes to be crossed after transformation;

optionally selecting two points in the crossed gene point positions in one chromosome code as selected gene segments, and combining the rest unselected gene segments with the other chromosome code to form a candidate gene library;

selecting genes from a candidate gene library and selecting gene segments to form a complete code, restoring the code length to the original length, and decoding to obtain an initial solution;

if the initial solution is a feasible solution, ending the cross operation and outputting a new individual in the filial generation population, otherwise, repeating the selection and gene recombination treatment of the selected gene segment until the initial solution is the feasible solution.

It can be understood that, for the crossover operation, aiming at the problem that the crossover operation is difficult to perform between chromosomes of different lengths, the present embodiment designs a two-point crossover method based on chromosome coding length transformation, which ensures the generation of new offspring population individuals.

Specifically, the algorithm input for the crossover operation encodes the two chromosomes to be crossed

And

firstly, for input

And

length conversion is carried out, and expanded chromosome codes to be crossed are determined

And

the gene site of the gene site capable of double-point crossing operation. Then, at

Optionally two points of the cross-over gene locus of (2) are selected as gene segments

And will start a bit

The remaining genes and

combining to form candidate gene library

Finally, from the candidate gene library

In the selection of the appropriate gene and the selected gene fragment

Form complete codes

Restore it to original length

Decoding to obtain an initial solution

If it is

If the solution is feasible, the cross operation is ended, and a new individual in the filial generation population O is output

If it is

Is not feasible, thenRepeating the gene fragment selection and recombination stages until

So far as feasible.

Furthermore, in the stage of transformation of the coding length of the chromosomes, in particular, in the first step, the number h of helicopters is grouped by using the greatest common factor of the number of helicopters required for all the training tasks as a unit _min Assigning values and calculating the code length of the expanded chromosome according to the values

The total number of helicopters required by all training tasks corresponding to each chromosome code in the population is always equal to the total number of helicopters which can ensure the action of all LHDs in the total action fluctuation, so that the extended length of each chromosome code can be ensured to be equal to the total number of helicopters

Second step, for input to be cross-coded

And

according to which each gene is subjected to length conversion before being coded

And

the occupied length of the material is as follows:

to code

And

the extension code is obtained after length conversion

And

third step, construct length and

and

same set of interdigitable front end points

And set of cross-linkable back endpoints

For the

Middle (i) ^Full Each gene position, if and only if

And

when all are respectively the first coding gene corresponding to one of the training tasks in the respective expansion codes

The rest gene positions take the value of 0, and the same is true if and only if

And

all are the last coding gene corresponding to one training task in respective extension codes

The rest gene positions are 0.

Further, in the gene fragment selection stage: first, a set of cross-capable front end points is randomly selected

And set of cross-linkable back endpoints

Gene site location i with median value of 1 ^Front And i ^Tail Require i ^Front And i ^Tail Satisfy i ^Front ＜i ^Tial Otherwise, repeating the random selection operation (i) ^Front ＝1,i ^Tail ＝L ^Full The existence of the condition of (b) guarantees the condition i ^Front ＜i ^Tial Must be satisfied) as the front and back end points of the selected gene fragment. A second step of

As selected gene fragment

Third step, use of

The gene composition candidate gene library of (1)

And delete

And (c) a gene that is identical to a repeat in the selected gene fragment.

Further, in the gene recombination stage: first, build and expand codes

And

equal length child spreading code

Selecting gene segments

Expanding code as descendants

Ith of (2) ^Front To the ith ^Tail A bit. Second, expand encoding the descendants

Removing the selected gene fragment

All remaining gene sites outside

As the space to be inserted, for the ith in the space to be inserted ^Cross Individual gene sites, searching candidate gene library in order

All elements in (1) are selected to have a length corresponding to a single training task of all the constraints previously mentioned

As a child extension code

Ith of (2) ^Cross To the ith ^Cross +l ^Curr -position 1, and deleting the corresponding coding in the candidate gene library. Third step, let i ^Corss ←i ^Corss +|σ ^Curr Repeat the second step of this paragraph until

Until now. Fourthly, expanding and coding the newly generated offspring

Performing the inverse operation of the chromosomal length transform to obtain the normal form of the offspring code

Decoding to obtain

Finally, if

If feasible, outputting the offspring individuals

Otherwise, the candidate gene library is

The first training task in (a) is moved to the end, and the aforementioned second to fourth steps of this paragraph are repeated (likewise, i) ^Front ＝1,

The existence of a situation warrants the necessity of loop termination).

As shown in fig. 6, a specific flow diagram of the interleaving operation between one code and another code as shown in fig. 4 is given.

In an embodiment, the process of the change operation in step S13 may specifically include the following processing:

generating a random number through 0-1 binomial distribution with the probability of the set mutation probability, if the random number is 1, performing mutation, and otherwise, outputting the input initial chromosome code and the initial solution as a new individual;

randomly selecting two genes in the input initial chromosome codes to carry out position exchange, and carrying out decoding operation on newly generated chromosome codes after exchange to obtain a new solution;

if the new solution is feasible, outputting the offspring individuals, otherwise, performing the position exchange operation again until the corresponding new solution is feasible.

It is understood that this example provides a mutation method based on the exchange of positions of genes in the code, and specifically, the algorithm input of the mutation operation is the initial chromosomal code

Initial solution

And setting mutation probability P ^Mut . First, the passing probability is P ^Mut A distribution of two terms of 0-1 generates a random number B (1, P) ^Mut ) If 1, mutation is performed, otherwise, mutation will be performed

And outputting as a new individual. Second, randomly selecting input chromosome codes

The two genes in the gene pair are subjected to position exchange, and the newly generated chromosome is subjected to position exchange

Performing a decoding operation to obtain a new solution

If it is

If it is not feasible, the second step of this paragraph is resumed until the next step

So far as feasible. Finally, outputting the offspring individuals

In an embodiment, the process of the local optimization search operation in step S13 may specifically include the following processing:

assigning the current chromosome code as an input chromosome code, and then assigning the optimal code and the optimal solution corresponding to the optimal individual as an input chromosome code and a solution individual corresponding to the input chromosome code respectively;

setting the tabu list as an empty set and calculating the number of 2 types of neighborhood genes in the input chromosome code;

randomly selecting one class 2 neighborhood gene in the complete permutation code, randomly selecting a target sequence number as the target sequence number segment of the class 2 neighborhood gene for assignment, and decoding the updated current permutation complete code to obtain a current solution;

if the current solution is not feasible, repeating the random selection and arrangement of the 2 types of neighborhood genes in the complete codes and the decoding operation until the corresponding current solution is feasible;

judging the quality relation between the current solution and the optimal solution, if the pareto dominance of the first solution individual is determined, assigning a Boolean variable with an initial value of 0 to be 1, and respectively updating the optimal code and the optimal solution into a completely arranged code and the current solution;

if the Boolean variable value is 1, assigning values to the positions of the 2 types of neighborhood genes in the chromosome codes and the target sequence number segments and inserting the values into a tabu list;

and assigning the initial value of the Boolean variable to be 0 again, returning to the process of randomly selecting one type 2 neighborhood gene in the complete permutation code and randomly selecting a target sequence number to assign the value to the target sequence number segment of the type 2 neighborhood gene, decoding the updated current permutation complete code to obtain the current solution until the iteration number reaches the algorithm iteration number, and outputting a new offspring population individual.

It will be appreciated that, since the foregoing model assumes 2The number of the 2-type training task grouping helicopters is fixed, and the close range support tasks of a certain target, which are formed by the special helicopter large group grouping exceeding the fixed number, are indicated to be adjacent to a plurality of 2-type training tasks in terms of codes. Therefore, unlike class 1 training tasks, considering the case of fire assault in large fleet grouping or multiple formation for a target, class 2 training tasks in chromosome coding may not only occur but also occur multiple times, which makes class 2 neighborhood codes of length n have P ⁿ The range of the defined domain is greatly increased by the possible assignment. In order to solve the problem, a local optimization method based on tabu search for the 2-type neighborhood is provided, and the algorithm search efficiency is improved well.

Specifically, the algorithm input amount of the local optimization search is input chromosome coding

And its corresponding decoded solution body

Tabu list depth D _T And the number of algorithm iterations N. First, the current code is assigned as

Optimal encoding corresponding to optimal individuals

And optimal solution

Are respectively assigned as

And

defining the tabu list as empty set phi, calculating input code

Number of middle 2-type neighborhood genes

Second, randomly selecting and arranging complete codes

A class 2 neighborhood gene of

And randomly selecting a target sequence number p as the target sequence number segment of the gene

Assigning value, and then coding the updated current code

Performing decoding operation to obtain current solution

If it is

If not, the second step of this paragraph is repeated until

So far as feasible.

Thirdly, judging the current solution

And the optimal solution

If the first solution is good or bad

Pareto dominance (Pareto Dominate)

(is described as

If and only if

Wherein

) Then the initial value is 0 Boolean variable eta _T Assign a value of 1 and encode the optimum

And an optimal solution

Are respectively updated to

And

in addition, if the list L is contraindicated _T Has not inserted 2 kinds of neighborhood gene

Any assignment of (a) will also be applied to the Boolean variable η _T The value is assigned to 1.

The fourth step, if the Boolean variable eta _T If the value is 1, the neighborhood genes of class 2 are selected

Inserting a tabu list L at position i in chromosome and its target sequence number segment assignment p _T In the specific insertion process, if the tabu list L is forbidden _T Length | L _T I has reached the assumed upper bound D _T If so, delete the tabu list L first _T The stack bottom element of (1), then insert { i, p } from the stack top if | L _T |＜D _T Then insert { i, p } directly into L _T In (1). Finally, the Boolean variable eta is given again _T Assigning an initial value of 0, repeating the second step to the fourth step until the algorithm is iterated for N times, and outputtingGenerating new filial generation population individuals

Furthermore, the reference points are used as the basis of space decomposition and environment evolution in the multi-objective evolutionary algorithm, and have decisive influence on the optimization performance of the algorithm. Specifically, the reference point generation can be performed based on a traditional reference point generation method, and is used for composition of a new generation of population, then, the next iteration optimization is performed until the iteration times of the population reach the set maximum iteration times, and finally, optimized amphibious task planning scheme data is output.

In some embodiments, after case simulation is performed by using the method provided by the application, the result shows that the method provides an initial solution set generation operation and a local optimization operation on the population update iteration mechanism level, and the rapid planning effect of the training task of the efficient and practical amphibious helicopter is fully achieved. In practical engineering application, aiming at optimization contents such as target selection, time sequence arrangement and the like of helicopter training tasks, an amphibious helicopter training task planning model is constructed and expressed as a multi-target optimization model; in order to effectively evaluate the promotion effect of the helicopter training mission on the integral situation development of the landing action, a simulation-based landing action efficiency evaluation model is constructed by using the related ideas of system efficiency evaluation, and the objective function of an amphibious helicopter training mission planning model is calculated through the model; the method is characterized in that innovation is respectively carried out on the level of a population updating iteration mechanism and the level of a sequencing selection mechanism, the provided algorithm has excellent performance in convergence speed and diversity, and reliable reference is provided for training task planning of a helicopter in a landing operation.

It should be understood that although the various steps in the flow diagrams of fig. 1 and 3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of the steps of fig. 1 and 3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

As shown in fig. 7, in an embodiment, a multi-objective optimization-based helicopter mission planning system 100 is further provided, which includes a parameter obtaining module 11, a model calling module 12, a population constructing module 13, a descendant generating module 14, a merging calculation module 15, an aggregation generating module 16, a new population module 17, and an iteration control module 18. Wherein:

the parameter acquiring module 11 is configured to acquire a known task parameter; the task parameters comprise the total number of the platforms, the total wave frequency of helicopter operation, the solution space dimensionality, the simplex partition parameter and the maximum iteration number. The model calling module 12 is used for calling the constructed amphibious helicopter for training task planning model; the constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and the optimization objective functions of the amphibious helicopter training mission planning model comprise a time-consuming objective function of a minimum landing phase, a minimum manpower loss objective function and a ground threat objective function of a minimum helicopter.

The population construction module 13 is used for calculating the population scale and constructing an initial population for the amphibious helicopter training mission planning model based on the mission parameters. The offspring generation module 14 is configured to perform a crossover operation, a mutation operation, and a local optimization search operation on the parent population to generate an offspring population. The merging calculation module 15 is configured to merge the generated child population and parent population to obtain a merged population, update the IDEAL point and the NADIR point according to elements in the solution set corresponding to the merged population, and calculate a normalized solution set according to the IDEAL point and the NADIR point. The set generating module 16 is configured to generate a set of reference points and a corresponding set of lyph solutions on the normalized solution set. The new population module 17 is configured to assign each individual in the standardized solution set to a littoral solution set by using a clustering method based on an interior angle measurement method, perform non-dominated sorting operation in each littoral solution set based on a scaled sorting method, and select an individual corresponding to an element ranked at the top to form a new generation population. The iteration control module 18 is used for outputting the training mission planning scheme data of the amphibious helicopter when the population iteration times reach the maximum iteration times.

The helicopter mission planning system 100 based on multi-objective optimization provides an amphibious helicopter training mission planning model on the basis of reasonable assumption of a real landing training scene by extracting and analyzing main mission modes and characteristics of a helicopter in an amphibious landing operation, and expresses the amphibious helicopter training mission planning model as a multi-objective optimization model, and aims to minimize time consumption of landing actions, personal casualties of own parties and air defense fire threat of own helicopter to blue. The amphibious helicopter training task planning model considers specific practical constraints of multiple platforms which can be used as training resources, training and guaranteeing the whole-link task stage time sequence. A new initial solution generation, crossing, variation and local optimization method is provided on the aspect of a population updating iteration mechanism, the search efficiency of a code definition space is improved, and the optimization efficiency and the population diversity maintenance are well expressed, so that the aim of quickly planning training tasks of the high-efficiency and real-life amphibious helicopter is fulfilled.

In one embodiment, the modules of the multi-objective optimization-based helicopter mission planning system 100 can be further used to implement the further processing steps in the embodiments of the multi-objective optimization-based helicopter mission planning method.

For specific limitations of the helicopter mission planning system 100 based on multi-objective optimization, reference may be made to the corresponding limitations of the helicopter mission planning method based on multi-objective optimization, which are not described herein again. The various modules of the multi-objective optimization-based helicopter mission planning system 100 described above may be implemented in whole or in part in software, hardware, and combinations thereof. The modules may be embedded in a hardware form or a device independent of a specific data processing function, or may be stored in a memory of the device in a software form, so that a processor can call and execute operations corresponding to the modules, where the device may be, but is not limited to, various computer devices existing in the art.

In one embodiment, there is also provided a computer device comprising a memory and a processor, the memory storing a computer program, the processor when executing the computer program implementing the steps of: acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave times of helicopter operation, solution space dimensionality, simplex segmentation parameters and the maximum iteration times; calling the constructed amphibious helicopter for training a task planning model; constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and an optimization objective function of the amphibious helicopter training mission planning model comprises a time-consuming objective function of a minimum landing phase, a minimum manpower loss objective function and a ground threat objective function of the minimum helicopter;

calculating the population scale and constructing an initial population for the amphibious helicopter training task planning model based on the task parameters; performing cross operation, mutation operation and local optimization search operation on the parent population to generate a child population; combining the generated child population and the parent population to obtain a combined population, updating IDEAL points and NADIR points according to elements in a solution set corresponding to the combined population, and calculating to obtain a standardized solution set according to the IDEAL points and the NADIR points; generating a reference point set and a corresponding set of the Liji solution sets on the standardized solution set; distributing each individual in the standardized solution set to a lyre solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each lyre solution set based on a scaling sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation of population; and returning to the step of performing cross operation, mutation operation and local optimization search operation on the parent population to generate the offspring population, and outputting training task planning scheme data of the amphibious helicopter until the iteration number of the population reaches the maximum iteration number.

It is understood that the computer device includes, in addition to the memory and the processor, other software and hardware components not listed in this specification, which may be determined according to the model of the specific computer device in different application scenarios, and detailed descriptions are not listed in this specification.

In one embodiment, the processor when executing the computer program may further implement the additional steps or sub-steps of the embodiments of the method for helicopter mission planning based on multi-objective optimization.

In one embodiment, there is also provided a computer readable storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of: acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave times of helicopter operation, solution space dimensionality, simplex segmentation parameters and the maximum iteration times; calling the constructed amphibious helicopter training task planning model; constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and an optimization objective function of the amphibious helicopter training mission planning model comprises a time-consuming objective function of a minimized landing phase, a minimized manpower loss objective function and a target function of ground threat of a minimized helicopter;

calculating the population scale of the amphibious helicopter training task planning model based on the task parameters and constructing an initial population; performing cross operation, mutation operation and local optimization search operation on the parent population to generate a child population; combining the generated child population and the parent population to obtain a combined population, updating IDEAL points and NADIR points according to elements in a solution set corresponding to the combined population, and calculating to obtain a standardized solution set according to the IDEAL points and the NADIR points; generating a reference point set and a corresponding set of the Liji solution sets on the standardized solution set; distributing each individual in the standardized solution set to a radix-based solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each radix-based solution set based on a scaled sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation of population; and returning to the step of performing cross operation, mutation operation and local optimization search operation on the parent population to generate the offspring population, and outputting training task planning scheme data of the amphibious helicopter until the iteration number of the population reaches the maximum iteration number.

In one embodiment, the computer program, when executed by the processor, further implements the additional steps or sub-steps of the embodiments of the method for helicopter mission planning based on multi-objective optimization described above.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware that is instructed by a computer program, and the computer program may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus DRAM (RDRAM), and interface DRAM (DRDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the spirit of the present application, and all of them fall within the scope of the present application. Therefore, the protection scope of the present patent should be subject to the appended claims.

Claims

1. A helicopter mission planning method based on multi-objective optimization is characterized by comprising the following steps:

acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave times of helicopter operation, the solution space dimensionality, the simplex segmentation parameters and the maximum iteration times;

calling the constructed amphibious helicopter training task planning model; the constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and the optimization objective function of the amphibious helicopter training mission planning model comprises a time-consuming objective function of a minimum landing phase, a minimum manpower loss objective function and a ground threat objective function of the minimum helicopter;

combining the generated child population and the parent population to obtain a combined population, updating an IDEAL point and an NADIR point according to elements in a solution set corresponding to the combined population, and calculating according to the IDEAL point and the NADIR point to obtain a standardized solution set;

distributing each individual in the standardized solution set to the edge solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each edge solution set based on a scaled sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation of population;

and returning to the step of executing the cross operation, the mutation operation and the local optimization searching operation on the parent population to generate the offspring population, and outputting training task planning scheme data of the amphibious helicopter until the iteration number of the population reaches the maximum iteration number.

2. A helicopter mission planning method based on multi-objective optimization according to claim 1, characterized by the chromosome coding scheme adopted in the population iteration process being:

decomposing all training tasks meeting the condition of setting the number of helicopter marshalls into a single task list with the number of helicopter marshalls equal to 1 and adopting discrete double-row chromosome coding;

adding a gap gene in the chromosome coding of the task list;

the chromosome decoding mode adopted in the population iteration process is as follows:

converting chromosome codes of the task list into an action list representing the action plan of each helicopter;

obtaining a list of initiating moments of vertical login tasks and close range support tasks of each target through the going-out list;

according to the play list, the initiation time list and a predetermined training plan of a plane landing team, the improved Lankaster model is used for calculating the change of the force of each target node of the landing field along with time, the state vector of the landing field node is obtained, the progress direction of the landing action is deduced through the landing field state transfer model, and the achievement situation of the training purpose and the training task performance index set are determined.

3. A method for multi-objective optimization-based helicopter mission planning according to claim 3, wherein the process of converting to said play list comprises:

calculating the number of all vertical login teams delivered by each vertical login task in the chromosome code of the task list and the number of various helicopters required by each vertical login task and the short-distance support task;

selecting 1 class training tasks meeting training resource constraint in the task list according to the number of all vertical landing teams and the number of all classes of helicopters, assigning values to elements in the action list according to a set assignment formula, and adding the executed tasks to an executed task list according to the original list sequence; the class 1 training task is the vertical login task;

after all class-1 training tasks meeting training resource constraints are inserted into the action list, selecting class-2 training tasks meeting the training resource constraints in the task list according to the number of all vertical landing squads and the number of all classes of helicopters, and correspondingly updating the action list and the executed task list; the class 2 training task is the close range support task;

the solving process of the initiation time list comprises the following steps:

according to the task targets of the training tasks in the action list and the distribution condition of target air defense nodes in a landing field, planning a route set with the minimum risk from the helicopter to each task target by utilizing Dijkstra algorithm;

and solving by utilizing the action list and the route set to obtain the initiating moment list of the task execution stage of each training task.

A process for determining the set of training task performance indicators, comprising:

according to the initiation time list and a predetermined training plan of a plane landing team, landing site nodes which are confronted at each simulation time are found, the labor loss and the time consumption of each landing site node in confrontation with both parties are calculated, corresponding landing site node state vectors are updated according to landing site state transfer conditions, numerical integration operation is carried out on the actual force vectors of the manpower of both parties at each target node by setting simulation step lengths until the landing site node state vectors meet a predetermined target of a landing site stage opened up by landing actions or the landing site nodes cannot reach the training target;

and (4) counting the total consumption time of the stage of opening the landing field, the accumulated manpower loss of the vertical landing sub-team and the plane landing team and the bearing risk of all helicopters executing the training tasks to form the training task efficiency index set.

4. A helicopter mission planning method based on multi-objective optimization according to any one of claims 1 to 3, characterized by the process of constructing an initial population comprising:

determining the total number of tasks contained in the target chromosome according to the helicopter number constraint; the target chromosome is a chromosome comprising elements in the class 1 training task set;

constructing an arrangement set comprising all arrangement forms of the corresponding elements in the class 1 training task set in the chromosome with the length being the total task number based on the total task number and the task number contained in the corresponding elements in the class 1 training task set;

for each element in the permutation set, taking the residual space in the chromosome corresponding to the element as a 2-type neighborhood and randomly assigning values to obtain a permutation complete code; the 2-type neighborhood is a neighborhood where a gene corresponding to the 2-type training task is located;

carrying out chromosome decoding on the permutation integral codes to obtain corresponding permutation target values;

and returning to the step of determining the total number of tasks contained in the target chromosome according to the number constraint of the helicopters until each task contained in each element in the arrangement set is traversed, and outputting the initial population containing an initial coding set and an initial solution set.

5. A method for multi-objective optimization-based helicopter mission planning according to any one of claims 1 to 3 and wherein the process of performing crossover operations on a parent population comprises:

selecting two optional points in the crossed gene point positions in one chromosome code as selected gene segments, and combining the rest unselected gene segments with the other chromosome code to form a candidate gene library;

selecting genes from the candidate gene library and the selected gene segments to form a complete code, restoring the code length to the original length, and decoding to obtain an initial solution;

if the initial solution is a feasible solution, ending the cross operation and outputting a new individual in the filial generation population, otherwise, repeating the selection and gene recombination processing of the selected gene segment until the initial solution is the feasible solution.

6. A method for multi-objective optimization-based helicopter mission planning according to any one of claims 1-3, wherein said process of mutation operations comprises:

if the new solution is feasible, outputting the offspring individuals, otherwise, performing position exchange operation again until the corresponding new solution is feasible.

7. A method for multi-objective optimization-based mission planning for a helicopter as claimed in any one of claims 1 to 3, wherein said process of local optimal search operations comprises:

assigning a current chromosome code as an input chromosome code, and then assigning an optimal code and an optimal solution corresponding to an optimal individual as a solution individual corresponding to the input chromosome code and the input chromosome code respectively;

setting a tabu list as an empty set and calculating the number of 2 types of neighborhood genes in the input chromosome code;

randomly selecting a class 2 neighborhood gene in the complete permutation code, randomly selecting a target sequence number to assign a value to a target sequence number segment of the class 2 neighborhood gene, and decoding the updated current complete permutation code to obtain a current solution;

if the current solution is not feasible, repeating randomly selecting and arranging 2 types of neighborhood genes in the complete codes and decoding operation until the corresponding current solution is feasible;

judging the quality relation between the current solution and the optimal solution, if the pareto dominance of the first solution individual is determined, assigning a Boolean variable with an initial value of 0 to be 1, and respectively updating the optimal code and the optimal solution into the completely arranged code and the current solution;

if the Boolean variable value is 1, assigning the positions of 2 types of neighborhood genes in the chromosome codes and the target sequence number segments and inserting the assignment into the tabu list;

and assigning the initial value 0 to the Boolean variable again, returning to the process of randomly selecting one type 2 neighborhood gene in the complete permutation code and randomly selecting a target sequence number to assign a target sequence number segment of the type 2 neighborhood gene, decoding the updated current permutation complete code to obtain a current solution, and outputting a new offspring population until the iteration number reaches the algorithm iteration number.

8. A multi-objective optimization-based helicopter mission planning system, comprising:

the parameter acquisition module is used for acquiring known task parameters; the task parameters comprise the total number of the platforms, the total wave times of helicopter operation, the solution space dimensionality, the simplex segmentation parameters and the maximum iteration times;

the model calling module is used for calling the constructed amphibious helicopter for training task planning model; the constraint conditions of the amphibious helicopter training mission planning model comprise vertical landing squad quantity constraint, helicopter quantity constraint, platform guarantee resource constraint, mission phase time sequence constraint based on execution sequence and mission phase time sequence constraint based on platform deck scheduling, and the optimization objective function of the amphibious helicopter training mission planning model comprises a time-consuming objective function of a minimum landing phase, a minimum manpower loss objective function and a ground threat objective function of the minimum helicopter;

the population construction module is used for calculating the population scale of the amphibious helicopter training task planning model based on the task parameters and constructing an initial population;

a merging calculation module, configured to merge the generated child population and the parent population to obtain a merged population, update an IDEAL point and an NADIR point according to elements in a solution set corresponding to the merged population, and calculate a normalized solution set according to the IDEAL point and the NADIR point;

a set generating module, configured to generate a set of reference points and a set of corresponding solutions for interest on the standardized solution set;

the new population module is used for distributing each individual in the standardized solution set to the interest base solution set by using a clustering method based on an interior angle measurement method, performing non-dominated sorting operation in each interest base solution set based on a scaled sorting method, and selecting the individual corresponding to the element with the top rank to form a new generation population;

9. A computer arrangement comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, carries out the steps of the multiobjective optimization based helicopter mission planning method of any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the multiobjective optimization based helicopter mission planning method of any one of claims 1 to 7.