CN116432872B - HHO algorithm-based multi-constraint resource scheduling method and system - Google Patents
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Abstract
The embodiment of the specification provides a multi-constraint resource scheduling method and system based on a HHO algorithm, wherein the method comprises the following steps: constructing a mathematical analysis model of air defense resource coverage in multiple scenes according to the relative position relation among an air defense unit, a target and a defense target in a combined air defense background, analyzing the relative position relation between a preparation array and a basic array based on the mathematical analysis model, and determining an optimal route length analysis formula and an overall defense capacity analysis formula; constructing a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis formula and the overall defensive capability analysis formula, and respectively determining an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene; and solving the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization target and the multi-constraint condition, and outputting the position of the prey as an optimal solution to obtain an optimal resource scheduling scheme.
Description
Technical Field
The present document relates to the technical field of resource scheduling and operation planning, and in particular, to a multi-constraint resource scheduling method and system based on an HHO algorithm.
Background
The multi-constraint resource scheduling problem refers to reasonably planning the operation and use schemes of resources under the complex background constraint condition so as to achieve the efficient utilization of the resources. With the development of technology, the number of air defense resources and targets under the integrated joint task all show a rapid rising trend, and particularly the evolution is aggravated by the large-scale use of unmanned equipment. The huge task amount and resource amount need a scheduling method which is suitable for the scale, so that the resource scheduling task under the multi-constraint condition can be completed better. The multi-constraint resource scheduling relates to the characteristics of multiple resource types, complex constraint, huge scale, large solving calculation amount and complex evaluation system, a classical model mainly comprises a constraint satisfaction model and a 0-1 planning model, a scheduling model based on planning, a Petri network, a mixed integer planning model and the like, and a common scheduling method mainly comprises a genetic algorithm, an improved algorithm thereof, a heuristic algorithm and the like.
For the traditional air defense resource scheduling method under the multi-constraint condition, the relative relation between the schedulable resource and the defending target and the relative relation between the schedulable resource and the protected target are respectively analyzed and processed, and the scheduling scene tends to be complex along with the upgrading and development of the attack and defense technology, so that the analysis method is not suitable for resource scheduling under the multi-constraint condition.
For the initial population generation method in the multi-constraint resource scheduling model application step, the traditional method mainly adopts a random generation mode, the influence of the initial population on iterative optimization is not considered, the problem solving time complexity is high, and meanwhile, the convergence accuracy is low.
For solving algorithms of multi-constraint resource scheduling problems, traditional algorithms such as genetic algorithms, particle swarm algorithms, simulated annealing algorithms and the like have low convergence speed and are easy to sink into local optimum when complex constraint conditions are processed, and have certain limitations on the multi-constraint resource scheduling problems.
Therefore, the problems of multiple resource types, complex constraint, huge scale, large calculation amount of solution and complex evaluation system exist for multi-constraint resource scheduling in the prior art.
Disclosure of Invention
The invention aims to provide a multi-constraint resource scheduling method and system based on a HHO algorithm, and aims to solve the problems in the prior art.
The invention provides a multi-constraint resource scheduling method based on a HHO algorithm, which comprises the following steps:
constructing a mathematical analysis model of air defense resource coverage in multiple scenes according to the relative position relation among an air defense unit, a target and a defense target in a combined air defense background, analyzing the relative position relation between a preparation array and a basic array based on the mathematical analysis model, and determining an optimal route length analysis formula and an overall defense capacity analysis formula;
constructing a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis formula and the overall defensive capability analysis formula, and respectively determining an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene;
and solving the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization target and the multi-constraint condition, and outputting the position of the prey as an optimal solution to obtain an optimal resource scheduling scheme.
The invention provides a multi-constraint resource scheduling system based on a HHO algorithm, which comprises the following steps:
the complex constraint analysis module is used for constructing mathematical analysis models of air defense resource coverage in multiple scenes according to the relative position relations among the air defense units, the targets and the defense targets in the combined air defense background, analyzing the relative position relations between the preparation array and the basic array based on the mathematical analysis models, and determining an optimal route length analysis formula and an overall defense capacity analysis formula;
the multi-constraint resource scheduling optimization model construction module is used for constructing a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis model and the overall defensive capability analysis model, and respectively determining an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene;
and the HHO module is used for solving the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization target and the multi-constraint condition, and outputting the position of the prey as an optimal solution to obtain an optimal resource scheduling scheme.
The embodiment of the invention also provides electronic equipment, which comprises: the method comprises the steps of a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the multi-constraint resource scheduling method based on the HHO algorithm.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores an information transmission implementation program, and the program is executed by a processor to implement the steps of the multi-constraint resource scheduling method based on the HHO algorithm.
By adopting the embodiment of the invention, the problems of low convergence accuracy, high calculation complexity and low efficiency of the conventional resource scheduling algorithm in the prior art are solved, and the technical scheme of the embodiment of the invention has better optimizing capability and convergence speed, and realizes more efficient resource scheduling with lower calculation cost.
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For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description that follow are only some of the embodiments described in the description, from which, for a person skilled in the art, other drawings can be obtained without inventive faculty.
FIG. 1 is a flow chart of a multi-constraint resource scheduling method based on the HHO algorithm according to an embodiment of the invention;
FIG. 2 is a schematic illustration of multi-constraint resource scheduling based on a HHO algorithm in accordance with an embodiment of the invention;
FIG. 3 is a diagram of relative position of schedulable resources according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a situation of a relative position of an attack target and a protected target in a first scene according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a situation of a relative position of an attack target and a protected target in a second scene according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a third scenario featuring a relative location of an attack target and a protected target according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a situation of a relative position of an attack target and a protected target in a fourth type of scenario according to an embodiment of the present invention;
FIG. 8 is a flowchart of a detailed process for a HHO algorithm to solve a multi-constraint resource scheduling method in accordance with an embodiment of the invention;
FIG. 9 is a schematic diagram of an example of the overall vector in the case of progressive fast soft wrap of an embodiment of the present invention;
FIG. 10 is a schematic diagram of an example of the overall vector in the case of progressive fast hard wrapping of an embodiment of the present invention;
FIG. 11 is a schematic diagram of a HHO algorithm-based multi-constraint resource scheduling system in accordance with an embodiment of the invention;
fig. 12 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to solve the problems in the prior art, the embodiment of the invention discloses a multi-constraint resource scheduling method based on a Harris eagle optimization algorithm (Harris Hawks Optimizer, which is called HHO for short), which comprises five steps of multi-constraint condition analysis, multi-constraint scheduling problem model construction, initial population screening generation and HHO algorithm model solving in a complex scene. The method specifically comprises the following steps: and constructing an air defense resource coverage mathematical model under a multi-class scene according to the relative position relation among the air defense unit, the target and the defense target under the combined air defense background. And determining an optimization target, and constructing a multi-constraint resource scheduling optimization model according to the constructed mathematical model and the corresponding constraint conditions. And (3) carrying out preliminary screening on the initial solution according to the optimization objective and the objective function value, and selecting a better initial solution. And finally, designing an HHO algorithm optimization flow under the multi-constraint resource scheduling problem, and solving the constructed resource scheduling optimization model to obtain an optimal resource scheduling scheme.
In order to enable a person skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
Method embodiment
According to an embodiment of the present invention, there is provided a multi-constraint resource scheduling method based on an HHO algorithm, and fig. 1 is a flowchart of the multi-constraint resource scheduling method based on the HHO algorithm according to the embodiment of the present invention, as shown in fig. 1, where the multi-constraint resource scheduling method based on the HHO algorithm according to the embodiment of the present invention specifically includes:
step 101, constructing a mathematical analysis model of air defense resource coverage in multiple scenes according to the relative position relation among an air defense unit, a target and a defense target in a combined air defense background, analyzing the relative position relation between a preparation array and a basic array based on the mathematical analysis model, and determining an optimal route length analysis formula and an overall defense capacity analysis formula; specifically, according to the relative position relation among an air defense unit, a target and a defending target in a combined air defense background, constructing a mathematical analysis model of air defense resource coverage in four types of scenes, wherein an air defense unit which is used for scheduling is formed by connecting the attack direction of the defending target and the protected target; the attack direction of the defending target and the connecting line of the protected target are used as a second class scene through a defending unit which can be scheduled; the interception range of the attack direction of the defending target and the connection line of the protected target exceeding the schedulable defending resource is a third type scene; the protected target is located in the interception range of the available scheduling resources and is a fourth type of scene.
102, constructing a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis formula and the overall defensive power analysis formula, and respectively determining an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene; specifically, the relative position relationship among an air defense unit, a target and a defending target is considered by adopting a divide-and-conquer concept, the air defense unit which can be scheduled is set as a first type scene, the defending unit which can be scheduled is set as a second type scene, the defending unit which can be scheduled is set as a connecting line of the defending target and the defending target, the interception range of the defending target, which exceeds the schedulable defending resource, is set as a third type scene, the defending target is set as a fourth type scene, and the corresponding optimization targets and constraint conditions under the first type scene, the second type scene, the third type scene and the fourth type scene are respectively determined.
And step 103, solving the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization target and the multi-constraint condition, and outputting the position of the prey as an optimal solution to obtain an optimal resource scheduling scheme. Specifically, a certain number of initial solutions are generated based on the multi-constraint resource scheduling optimization model, M initial solutions are initially screened according to an optimization target and a target function value, and N optimal initial solutions are selected to be used as an HHO initial population, wherein N is smaller than M; and designing an HHO algorithm optimization flow under the multi-constraint resource scheduling problem, selecting initial parameters, calculating the fitness value of each individual, selecting an optimal value as a hunting position, updating the HHO individual position, then judging whether convergence conditions are met or not again, and solving the multi-constraint resource scheduling optimization model to obtain an optimal resource scheduling scheme.
The technical scheme of the embodiment of the invention adopts the integrated analysis processing of the scheduling resource, the defending target and the protected target, realizes the unified processing of the scheduling resource and the scheduling target of the target, and improves the efficiency of multi-constraint resource scheduling. Aiming at the problem that the prior knowledge is ignored in the iteration process to cause higher calculation times, the embodiment of the invention carries out preliminary screening on the initial solution according to the objective function value of the optimization target, and selects the optimal initial solution to evolve the optimization target. Firstly, M initial solutions are randomly generated, the fitness value of each initial solution is calculated, and the first N (N < M) initial solutions with the highest fitness values are selected as HHO initial populations. According to the embodiment of the invention, the Harish eagle optimization algorithm HHO is adopted to solve the multi-constraint optimization model, and the HHO is used as a group intelligent optimization algorithm for simulating the Harish eagle hunting process in nature, so that the algorithm has a simple structure and few parameters, and can effectively avoid sinking into local optimum.
The following describes the above technical solution of the embodiment of the present invention in detail.
The embodiment of the invention provides a multi-constraint resource scheduling method based on a HHO algorithm. And the efficiency of resource scheduling under the multi-constraint condition is improved, and the computational complexity is reduced. In order to achieve the technical purpose and achieve the technical effect, the embodiment of the invention is realized by the following technical scheme:
step 1), determining relative coverage as a classification basis according to the relative position relation among an air defense unit, a target and a defense target in a combined air defense background, and constructing an air defense resource coverage mathematical model in four types of scenes;
step 2) analyzing the relative position relation between the preparation array and the basic array, and determining an optimal route length analytic expression and an overall defensive ability analytic expression;
step 3) taking the relative position relation among the air defense unit, the target and the defending target into consideration by adopting a divide-and-conquer idea, and setting a first scene of the air defense unit which can be scheduled when the attack direction of the defending target and the connecting line of the protected target do not pass through;
step 4), setting the condition that the connection line between the attack direction of the defending target and the protected target passes through a defending unit available for dispatching, and defining the condition as a second class scene;
step 5), setting the condition that the connection line between the attack direction of the defending target and the protected target exceeds the interception range of the schedulable defending resource, and defining the condition as a third type scene;
step 6), setting the condition that a protected target is positioned in a range for intercepting a scheduling resource, and defining the condition as a fourth type scene;
step 7) respectively determining an optimization objective function and corresponding constraint conditions on the basis of the four types of scenes;
step 8) solving the scheduling optimization model constructed in the above steps by adopting an HHO algorithm;
step 9) taking the output prey position as an optimal solution to obtain an optimal resource scheduling scheme;
step 10) ends.
The technical scheme of the embodiment of the invention is further described in detail below with reference to the accompanying drawings and examples.
As shown in FIG. 2, the embodiment of the invention provides a multi-constraint resource scheduling method based on an HHO algorithm, which comprises a complex constraint analysis module, a multi-constraint resource scheduling optimization model construction module, an initial population screening generation module and an HHO algorithm model solving module.
The complex constraint analysis module is used for constructing an air defense resource coverage mathematical model under four types of scenes by taking relative coverage as a classification basis according to the relative position relation among the air defense unit, the target and the defense target under the combined air defense background, obtaining analysis expression of scheduling result evaluation, and quantitatively calculating the defensive capacity after completing resource scheduling.
The multi-constraint resource scheduling optimization model construction module is used for decomposing the problem according to the characteristics of complex constraint and large scale of multi-constraint resource scheduling, determining a resource scheduling problem optimization target, and constructing a multi-constraint resource scheduling optimization model according to the constructed mathematical analysis model and the constraint conditions under the corresponding complex scene.
The initial population screening generation module generates a certain number of M initial solutions, performs initial screening on the initial solutions according to the optimization target and the objective function value, and selects N optimal initial solutions (N < M).
The HHO algorithm model solving module is used for:
1. HHO algorithm optimization flow design under the multi-constraint resource scheduling problem;
2. setting initial parameters;
3. calculating fitness value of the individual and selecting the optimal value as a hunting position;
4. and after updating the HHO individual position, judging whether the convergence condition is met or not again, and solving the constructed resource scheduling optimization model to obtain an optimal resource scheduling scheme.
In combination with the above modules, the multi-constraint resource scheduling method based on the HHO algorithm according to the embodiment of the invention generally comprises the following steps:
step 1), a complex constraint analysis module constructs an air defense resource coverage mathematical model in four types of scenes by taking relative coverage as a classification basis according to the relative position relation among an air defense unit, a target and a defense target in a combined air defense background, obtains analysis expression of scheduling result evaluation, and quantitatively calculates the defensive capacity after resource scheduling is completed.
Step 2) analysis of the prepared array C i (x i ，y i ) And basic array B i （x bi ，y bi ) Relative positional relationship between prepared array C i (x i ，y i ) Is connected with a connecting road which is the shortest shortcut point M from the preparation matrix to the nearest road i (x mi ，y mi ) When the same preparation matrix is the same as the shortest shortcut of a plurality of roads, the number of connecting tracks is equal to the corresponding number of roads, and the specific relative relationship is shown in fig. 3.
According to the geometric relationship, when CM,i.e. the prepared array site C i Is a contact length of:
the optimal route length is as follows:
for quantitatively calculating the defensive capacity after the air defense resource scheduling calculation, defining the defensive capacity gamma as follows:
wherein θ is an effective interception angle, Φ represents a maximum included angle of an attack direction of a defending target, a represents an effective interception area, and S represents a maximum interception area of an air defense unit (a circle field with a firepower unit as a circle center and a reverse-guiding interception range as a radius).
And 3) considering the relative position relation among the air defense unit, the target and the defending target by adopting a divide-and-conquer idea, wherein the three are considered in four scenes, the first scene is shown in figure 4, and the connecting line of the defending target and the protected target does not pass through the air defense unit for dispatching.
The main function of the air defense unit is to provide air defense for important cities, and the defending capability of the air defense firepower unit for the important cities firstly considers the attack direction phi. For fire units, the turning and detour flight modes of the missile are not considered, the possible attack direction considers the red whole area, and the fire units are characterized by cosine theorem, phi i The analytical expression is:
in the formula ,Rmax and Rmin The upper and lower boundary vertices of the red region, respectively.
For the effective interception angle θ, it is available from the geometric relationship:
in the formula ,d1 Is calculated as follows:
from the above formula:
for the effective interception area A, the effective interception area should be increased as much as possible when the air defense unit selects the battle field, and the interception time window is increased. From the geometrical relationship:
namely:
thus, a first class of scenario overall defenses capability is available:
step 4) if the attack direction of the defending object and the connection line of the protected object pass through the schedulable unit, consider a second class of scenario, as shown in fig. 5.
For the effective interception angle θ, it is available from the geometric relationship:
for the effective interception area a, it is possible to obtain from the geometrical relationship:
therefore, defenses in the second class of scenarios are available:
step 5) if the connection line between the attack direction of the defending target and the protected target exceeds the interception range of the schedulable defending resource, consider a third type of scenario, as shown in fig. 6.
For the effective interception angle θ, it is available from the geometric relationship:
for the effective interception area a, i.e. a=s.
Therefore, defenses in a third class of scenarios are available:
step 6) if the protected target is located within the available scheduling resource interception range, consider a fourth scenario, as shown in fig. 7.
For an effective interception angle θ, θ=Φ, which is available from the geometric relationship.
For the effective interception area a, it is possible to obtain from the geometrical relationship:
for BO i The analysis can be obtained:
in the formula ,d3 Is M i To BO i Is a perpendicular to the line (c).
Therefore, defenses in a fourth class of scenarios are available:
step 7) on the basis of the four scenes, determining an optimization objective function and corresponding constraint conditions, and setting the maneuvering time of the ith air-fire prevention unit as t i The optimal transfer distance of the ith air-fire-proof unit is L i (including the contact way ll) i Distance l from road i ) The air-defense fire power unit runs at the speed v on the highway 1 At a link travel speed v 2 Then:
to simplify the calculation, v is taken 1 =v 2 =v, then:
in order to optimize the total defensive power of the air-fire unit, the objective function 1 is defined as:
to minimize the sum of the runtimes of the air-fire units, the objective function 2 is defined as:
the constraint is defined as:
step 8) adopting the HHO algorithm to solve the scheduling optimization model constructed in the steps, and adopting the HHO algorithm to solve the multi-constraint resource scheduling optimization model, wherein the flow of the flow is shown in fig. 8: the method comprises three stages of an exploration stage, a conversion stage and a development stage. In the exploration stage, all population individuals of the Harris eagle are in a waiting state, hunting objects are found in random places through acute eye tracking and hunting object detection, and position updating is carried out with probability q in iteration. The process of solving the multi-constraint resource scheduling optimization model by adopting the HHO algorithm is shown in fig. 8.
wherein ,Xrand Randomly selecting individuals in the current population, X rabbit X is the currently optimal individual m R is the average position of the current population 1 、r 2 、r 3 、r 4 The random numbers are 0-1, ub and lb are the upper and lower bounds of the population respectively, and N is the population number.
In the conversion phase, HHO calculates to change from global search optimum to local search optimum, and is controlled by escape energy factor E, and the formula is:
wherein ,E0 Is [ -1,1]The random number in between, T is the current iteration number, and T is the maximum iteration number.
In the development stage, after the target hunting object is found, the harris eagle forms a circle of attack around the hunting object, and the chance of sudden attack is waited. Since the prey may escape the enclosure, the harris eagle needs to be adjusted as necessary according to the behavior of the prey. To better simulate hunting behavior, the development phase is updated with four policies (Case 1-Case 4) and determines which policy to use by parameter E and a [0,1] random number.
(1) Soft enclosure
When |E| is 0.5 or more and r is 0.5 or more, the prey has enough energy to try to escape the enclosure by random jump, but eventually cannot escape, so the Harris eagle uses a soft enclosure to hunting, expressed as:
wherein DeltaX is the difference between the optimal individual and the current individual, r 5 And the random numbers are uniformly distributed in a range of 0 to 1, and J is the jumping distance in the escape process of the rabbits.
(2) Hard enclosure
When |E| <0.5 and r > 0.5, the prey has neither enough energy to break out nor the opportunity to escape, so the Harris eagle uses a hard surround to hunting, expressed as:
(3) Progressive rapid soft wrap
When |E| is greater than or equal to 0.5 and r <0.5, the prey has the opportunity to escape from the enclosure with sufficient escape energy so the Harris eagle needs to form a more intelligent soft enclosure before attack, as shown in FIG. 9. The following two strategies are implemented. When the first policy is invalid, the second policy is executed.
The second policy updates the formula:
wherein: d problem dimension, S is a random vector, LF is a Levy flight function, and the formula is as follows:
wherein: l and m are random numbers which are uniformly distributed in a range of 0-1, and beta is a constant with a value of 1.5.
The phase update strategy is thus ultimately as follows:
(4) Progressive rapid hard wrap
When |E| <0.5 and r <0.5, the prey has the opportunity to escape, but the escape energy is insufficient, so the Harris eagle forms a hard enclosure before the attack, reducing their average distance from the prey, and the overall vector for the case of progressive rapid hard enclosure is shown in FIG. 10. Hunting was performed using the following strategy:
and outputting the hunting position as an optimal solution to obtain an optimal resource scheduling scheme.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
according to the embodiment of the invention, the HHO algorithm is used for solving the resource scheduling problem under the multi-constraint condition, the characteristics of complex model constraint and large solving calculation amount are considered, the constraint condition is discussed by classifying by adopting the divide-and-conquer idea, the resource scheduling mathematical model under various scenes is constructed, and the calculation complexity is reduced. In order to improve the algorithm solving efficiency, a HHO algorithm initial population screening generation strategy is introduced, screening is carried out according to fitness values on the basis of randomly generating initial solutions, then the initial population is determined, the optimal initial population is used for participating in the algorithm evolution and searching, and the model solving precision and efficiency are improved.
System embodiment
According to an embodiment of the present invention, there is provided a multi-constraint resource scheduling system based on HHO algorithm, and fig. 11 is a schematic diagram of the multi-constraint resource scheduling system based on HHO algorithm according to the embodiment of the present invention, as shown in fig. 11, where the multi-constraint resource scheduling system based on HHO algorithm according to the embodiment of the present invention specifically includes:
the complex constraint analysis module 110 is configured to construct a mathematical analysis model for air defense resource coverage in multiple scenes according to the relative position relationship among the air defense unit, the target and the defense target in the combined air defense background, analyze the relative position relationship between the preparation array and the basic array based on the mathematical analysis model, and determine an optimal path length analysis formula and an overall defense capability analysis formula; the method is particularly used for: according to the relative position relation among an air defense unit, a target and a defending target in the combined air defense background, constructing a mathematical analysis model of air defense resource coverage in four types of scenes, wherein an air defense unit which is used for scheduling and is not passed by a connecting line of the defending target and the protected target is a first type of scene; the attack direction of the defending target and the connecting line of the protected target are used as a second class scene through a defending unit which can be scheduled; the interception range of the attack direction of the defending target and the connection line of the protected target exceeding the schedulable defending resource is a third type scene; the protected target is located in the interception range of the available scheduling resources and is a fourth type of scene.
The multi-constraint resource scheduling optimization model construction module 112 is configured to construct a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis model and the overall defensive capability analysis model, and determine an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene respectively; the method is particularly used for: the relative position relation among an air defense unit, a target and a defending target is considered by adopting a dividing and controlling thought, the air defense unit which is used for dispatching and is not passed by a connecting line of the defending target and the protected target is set as a first type scene, the defending unit which is used for dispatching and is passed by the connecting line of the defending target and the protected target is set as a second type scene, the interception range of the connecting line of the defending target and the protected target exceeding the dispatchable defending resource is set as a third type scene, the interception range of the protected target being set as a fourth type scene, and the corresponding optimization targets and constraint conditions under the first type scene, the second type scene, the third type scene and the fourth type scene are respectively determined.
And the HHO module 114 is configured to solve the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization objective and the multi-constraint condition, and obtain an optimal resource scheduling scheme by using the output prey position as an optimal solution. The method specifically comprises the following steps: the initial population screening generation module is used for generating a certain number of initial solutions based on the multi-constraint resource scheduling optimization model, carrying out preliminary screening on M initial solutions according to an optimization target and a target function value, and selecting N optimal initial solutions as HHO initial populations, wherein N is less than M;
the HHO algorithm model solving module is used for designing an HHO algorithm optimizing flow under the multi-constraint resource scheduling problem, selecting initial parameters, calculating the fitness value of each individual and selecting an optimal value as a prey position, updating the HHO individual position, then judging whether convergence conditions are met or not again, and solving the multi-constraint resource scheduling optimizing model to obtain an optimal resource scheduling scheme.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood by referring to the description of the method embodiment, which is not repeated herein.
Device embodiment 1
An embodiment of the present invention provides an electronic device, as shown in fig. 12, including: a memory 120, a processor 122 and a computer program stored on the memory 120 and executable on the processor 122, which when executed by the processor 122 performs the steps as described in the method embodiments.
Device example two
Embodiments of the present invention provide a computer-readable storage medium having stored thereon a program for realizing information transmission, which when executed by the processor 122 realizes the steps as described in the method embodiments.
The computer readable storage medium of the present embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, etc.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (6)
1. The multi-constraint resource scheduling method based on the HHO algorithm is characterized by comprising the following steps of:
according to the relative position relation among an air defense unit, a target and a defending target in a combined air defense background, constructing a mathematical analysis model for air defense resource coverage in a plurality of types of scenes, analyzing the relative position relation between a preparation array and a basic array based on the mathematical analysis model, and determining an optimal route length analysis formula and an overall defending capability analysis formula, wherein the method specifically comprises the following steps:
according to the relative position relation among an air defense unit, a target and a defending target under the combined air defense background, constructing a mathematical analysis model of air defense resource coverage under a multi-class scene, analyzing the relative position relation between a preparation array Ci and a basic array Bi based on the mathematical analysis model, wherein the preparation array Ci is connected with a road closest to the preparation array Ci through a connecting channel, the connecting channel is a shortest shortcut point Mi from the preparation array to the nearest road, when the same preparation array is the same as the shortest shortcuts of a plurality of roads, the number of the connecting channels is equal to the number of corresponding roads, determining an optimal route length analysis formula according to a formula 1 and a formula 2, and determining an overall defending capability analysis formula according to a formula 3:
when CM+.T BM:
equation 1;
wherein ,namely, the length of the contact road of the prepared land Ci is given by (xi, yi) the position of the prepared land Ci, (xmi, ymi) the position of the shortest shortcut point Mi from the prepared land to the nearest road, i representing the number of lands;
equation 2;
wherein ,for the optimal route length between the preparation array and the basic array, (xi, yi) is the position of the preparation array Ci, (xbi, ybi) is the position of the basic array Bi, (xmi, ymi) is the position of the shortest shortcut point Mi from the preparation array to the nearest highway, i represents the number of arrays;
equation 3;
wherein, gamma is the defending ability, theta is the effective interception angle, phi represents the maximum included angle of the defending target attack direction, A represents the effective interception area, S represents the maximum interception area of the air defense unit, and the maximum interception area is a circular domain taking the fire power unit as the center of a circle and the reverse-guiding interception range as the radius;
constructing a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis formula and the total defensive capacity analysis formula, and respectively determining an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene, wherein the method specifically comprises the following steps:
taking the relative position relation among an air defense unit, a target and a defending target into consideration by adopting a divide-and-conquer concept, setting a defending unit which is used for dispatching and is not passed by a connecting line of the defending target and the protected target as a first type scene, setting a defending unit which is used for dispatching and is passed by the connecting line of the defending target and the protected target as a second type scene, setting an interception range of the connecting line of the defending target and the protected target exceeding a schedulable defending resource as a third type scene, setting a interception range of the protected target within the schedulable resource as a fourth type scene, and respectively determining corresponding optimization targets and constraint conditions under the first type scene, the second type scene, the third type scene and the fourth type scene;
and solving the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization target and the multi-constraint condition, and outputting the position of the prey as an optimal solution to obtain an optimal resource scheduling scheme.
2. The method of claim 1, wherein solving the multi-constraint resource scheduling optimization model by using HHO algorithm according to the optimization objective and multi-constraint condition to output a prey location as an optimal solution, and obtaining an optimal resource scheduling scheme specifically includes:
generating a certain number of initial solutions based on the multi-constraint resource scheduling optimization model, and primarily screening M initial solutions according to an optimization target and an objective function value, and selecting N optimal initial solutions as an HHO initial population, wherein N is smaller than M;
and designing an HHO algorithm optimization flow under the multi-constraint resource scheduling problem, selecting initial parameters, calculating the fitness value of each individual, selecting an optimal value as a hunting position, updating the HHO individual position, then judging whether convergence conditions are met or not again, and solving the multi-constraint resource scheduling optimization model to obtain an optimal resource scheduling scheme.
3. A HHO algorithm-based multi-constraint resource scheduling system, comprising:
the complex constraint analysis module is used for constructing a mathematical analysis model of air defense resource coverage under multiple scenes according to the relative position relation among an air defense unit, a target and a defense target under the combined air defense background, analyzing the relative position relation between a preparation array and a basic array based on the mathematical analysis model, and determining an optimal route length analysis formula and an overall defense capacity analysis formula, and specifically comprises the following steps:
according to the relative position relation among an air defense unit, a target and a defending target under the combined air defense background, constructing a mathematical analysis model of air defense resource coverage under a multi-class scene, analyzing the relative position relation between a preparation array Ci and a basic array Bi based on the mathematical analysis model, wherein the preparation array Ci is connected with a road closest to the preparation array Ci through a connecting channel, the connecting channel is a shortest shortcut point Mi from the preparation array to the nearest road, when the same preparation array is the same as the shortest shortcuts of a plurality of roads, the number of the connecting channels is equal to the number of corresponding roads, determining an optimal route length analysis formula according to a formula 1 and a formula 2, and determining an overall defending capability analysis formula according to a formula 3:
when CM+.T BM:
equation 1;
wherein ,namely, the length of the contact road of the prepared land Ci is given by (xi, yi) the position of the prepared land Ci, (xmi, ymi) the position of the shortest shortcut point Mi from the prepared land to the nearest road, i representing the number of lands;
equation 2;
wherein ,for the optimal route length between the preparation array and the basic array, (xi, yi) is the position of the preparation array Ci, (xbi, ybi) is the position of the basic array Bi, (xmi, ymi) is the position of the shortest shortcut point Mi from the preparation array to the nearest highway, i represents the number of arrays;
equation 3;
wherein, gamma is the defending ability, theta is the effective interception angle, phi represents the maximum included angle of the defending target attack direction, A represents the effective interception area, S represents the maximum interception area of the air defense unit, and the maximum interception area is a circular domain taking the fire power unit as the center of a circle and the reverse-guiding interception range as the radius;
the multi-constraint resource scheduling optimization model construction module is used for constructing a multi-constraint resource scheduling optimization model under a multi-class scene according to the mathematical analysis model, the optimal path length analysis model and the overall defensive capability analysis model, and respectively determining an optimization target and constraint conditions corresponding to the multi-constraint resource scheduling optimization model under the multi-class scene, and is specifically used for:
taking the relative position relation among an air defense unit, a target and a defending target into consideration by adopting a divide-and-conquer concept, setting a defending unit which is used for dispatching and is not passed by a connecting line of the defending target and the protected target as a first type scene, setting a defending unit which is used for dispatching and is passed by the connecting line of the defending target and the protected target as a second type scene, setting an interception range of the connecting line of the defending target and the protected target exceeding a schedulable defending resource as a third type scene, setting a interception range of the protected target within the schedulable resource as a fourth type scene, and respectively determining corresponding optimization targets and constraint conditions under the first type scene, the second type scene, the third type scene and the fourth type scene;
and the HHO module is used for solving the multi-constraint resource scheduling optimization model by adopting an HHO algorithm according to the optimization target and the multi-constraint condition, and outputting the position of the prey as an optimal solution to obtain an optimal resource scheduling scheme.
4. The system of claim 3, wherein the HHO module specifically comprises:
the initial population screening generation module is used for generating a certain number of initial solutions based on the multi-constraint resource scheduling optimization model, carrying out preliminary screening on M initial solutions according to an optimization target and a target function value, and selecting N optimal initial solutions as HHO initial populations, wherein N is less than M;
the HHO algorithm model solving module is used for designing an HHO algorithm optimizing flow under the multi-constraint resource scheduling problem, selecting initial parameters, calculating the fitness value of each individual and selecting an optimal value as a prey position, updating the HHO individual position, then judging whether convergence conditions are met or not again, and solving the multi-constraint resource scheduling optimizing model to obtain an optimal resource scheduling scheme.
5. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the HHO algorithm based multi-constrained resource scheduling method according to any of claims 1 to 2.
6. A computer-readable storage medium, wherein a program for implementing information transfer is stored on the computer-readable storage medium, and the program when executed by a processor implements the steps of the HHO algorithm-based multi-constraint resource scheduling method according to any one of claims 1 to 2.
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