CN117575299B - Collaborative multitasking distribution method based on improved particle swarm algorithm - Google Patents
Collaborative multitasking distribution method based on improved particle swarm algorithm Download PDFInfo
- Publication number
- CN117575299B CN117575299B CN202410067031.2A CN202410067031A CN117575299B CN 117575299 B CN117575299 B CN 117575299B CN 202410067031 A CN202410067031 A CN 202410067031A CN 117575299 B CN117575299 B CN 117575299B
- Authority
- CN
- China
- Prior art keywords
- task
- combat
- execution
- individual
- tasks
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002245 particle Substances 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 35
- 239000011159 matrix material Substances 0.000 claims abstract description 13
- 238000005457 optimization Methods 0.000 claims abstract description 12
- 125000003275 alpha amino acid group Chemical group 0.000 claims description 15
- 150000001875 compounds Chemical class 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 239000002243 precursor Substances 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000011423 initialization method Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims 1
- 238000012986 modification Methods 0.000 claims 1
- 238000004220 aggregation Methods 0.000 abstract 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- NUFBIAUZAMHTSP-UHFFFAOYSA-N 3-(n-morpholino)-2-hydroxypropanesulfonic acid Chemical compound OS(=O)(=O)CC(O)CN1CCOCC1 NUFBIAUZAMHTSP-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a cooperative multitasking distribution method based on an improved particle swarm algorithm, which comprises the following steps: obtaining co-operationNAssembly of individual combat unitsUAggregation ofUInformation of each combat unit is contained in the information; acquisition ofMAggregation of individual combat tasksTAggregation ofTEach combat task target information is contained in the system; establishing a cooperative multitask allocation model according to the weapon resource requirements of the combat task and weapon resources owned by the combat unit; coding any feasible solution in the cooperative multitasking distribution model in a two-dimensional mixing matrix mode; the method comprises the steps of adopting an improved particle swarm optimization algorithm to solve a collaborative multi-task distribution model, designing an initialization strategy based on group ordering to initialize particle swarms, adopting a particle update strategy based on cross evolution and a local search strategy based on commonality to update and iterate particle positions, and outputting all particle positions in elite solution after iteration is completed, namely a task distribution scheme.
Description
Technical Field
The invention belongs to the field of cooperative multi-task allocation, and particularly relates to a cooperative multi-task allocation method based on an improved particle swarm algorithm.
Background
Collaborative multitasking is a strategy for a list of combat tasks, aimed at assigning each task an appropriate combat unit to clarify the execution subject of each task. Automatic allocation and optimization of combat tasks is an important component in task planning, however, this increases the complexity of the command decisions due to the diversity, complexity and uncertainty of combat tasks, and the combat capability differences of the different combat units. Therefore, how to effectively consider the synergy between different combat forces and realize the automatic allocation of combat tasks becomes a difficult problem of an auxiliary decision-making system.
The collaborative multitask allocation method belongs to the complex combination optimization problem, and is required to model according to the fight units, the task targets and the association relations between the fight units and the task targets and to formulate a fight scheme. At present, research on combat task allocation is mainly focused on specific types of combat scenes, and combat forces, task targets and task execution modes in the scenes are clear, so mathematical modeling is easy to perform, and existing optimization algorithms are adopted for model optimization. Wang Yong et al analyze the association relation between unmanned aerial vehicle configuration and task allocation, design a double-layer solution algorithm based on an evolutionary algorithm, and obtain an accurate unmanned aerial vehicle configuration scheme; yu et al divide task allocation into upper layer task allocation and lower layer task sequence optimization, and designed simulated annealing-scattering algorithm for solving; wang Feng et al propose a multi-objective model which simultaneously considers unmanned aerial vehicle task income, task execution time and task execution cost, and designs a co-evolution-based hybrid variable multi-objective particle swarm optimization algorithm for solving; bao Ning and the like propose an MDP-based solution scheme for planning a ground attack combat action scheme of the unmanned aerial vehicle formation; aiming at the unmanned aerial vehicle collaborative reconnaissance problem, weizhaoian and the like, a multi-unmanned aerial vehicle collaborative reconnaissance model is established, and an improved MVO algorithm is provided for solving; liuHaishi et al propose a heterogeneous multi-UAV collaborative task planning algorithm combining LSO and RRT algorithms for the task planning problem of unmanned aerial vehicle air maneuver in a three-dimensional mountain area environment; chen Xia and the like provide a task allocation method for cooperatively striking the ground moving target by a plurality of unmanned aerial vehicles according to the problem of cooperative task allocation and flight path planning of the ground moving target, and effectively solve the problem by using an improved ant colony algorithm; zhangLin et al put forward the mathematical model that considers weapon attack return to the weapon cooperative attack goal problem, and designed an improved genetic algorithm to solve; gaoSheng and the like aim at the problem of multi-unmanned aerial vehicle heterogeneous target reconnaissance task allocation, the heterogeneous targets are divided into three targets of points, lines and planes according to the geometric characteristics of the heterogeneous targets, and a grouping ant colony optimization algorithm is provided for solving. In the research, the task objective and the combat effort are simple, the task decision of combat is focused on the cooperative coordination among different combat units, and the type and the size of combat capability owned by each unit are large in gap, which is the difficulty of combat task allocation, and the related research content is less.
Disclosure of Invention
Technical problems: in order to solve the problem that the diversified combat forces are difficult to reasonably distribute and apply, the model is improved in objective function and constraint conditions according to task requirements and combat capabilities of combat units, and a collaborative multi-task distribution model is established. In order to effectively solve the proposed model, a collaborative multitask allocation method based on an improved particle swarm algorithm is provided, each allocation scheme is coded by the algorithm, and an initialization strategy based on group ordering, a particle updating strategy based on cross evolution and a local search strategy based on commonality are introduced to improve the convergence and diversity of the algorithm. Simulation experiments are carried out through operational scene setting examples, and the result shows that the algorithm is far superior to the traditional NSGA-II and MOPSO algorithms in terms of convergence and diversity, and the effectiveness of the provided algorithm in solving the collaborative multi-task allocation problem is verified.
The technical scheme is as follows: in order to solve the above technical problems, the present invention provides a collaborative multi-task allocation method based on an improved particle swarm algorithm, which comprises the following steps:
step 1, obtaining a cooperative battleSet of individual combat units->Set->Information of each combat unit is contained in the information; acquisition->Set of individual combat tasks->Set->Each combat task target information is contained in the system;
step 2, establishing a cooperative multitask allocation model for weapon resource requirements and weapon resources owned by a fight unit according to the fight task;
step 3, solving a cooperative multitasking distribution model by adopting an improved particle swarm optimization algorithm;
step 4, updating and iterating the initialized particle positions by adopting a particle updating strategy based on cross evolution and a local searching strategy based on commonality;
and 5, outputting all particle positions in the solution set after iteration is completed, namely, a task allocation scheme.
Further, the specific method of step 1 is as follows:
1.1 Assume that the decomposition of the combat target for the upper combat is provided withTask list of individual combat tasksDivided intoNPersonal combat unit->；
1.2 Each task is expressed as，/>Wherein->Is task->Is a target location of (2); />Is task->Is performed in the same manner as the execution time of the first step; />Is a task/>Is required for the completion time of (2); />Is task->Capability requirement vector of (1), and hasWherein->Is task->For the firstvDemand for item weapon resources;
1.3 Each combat unit is represented asWherein->Is a combat unit->Is>Is a combat unit->Is>Is a combat unit->Weapon resource costs of->Is a combat unit->Owned weapon resource vector, and hasWherein->Is a combat unitPossess->Number of weapon resources of item size,/->，/> Representation of/>Different weapon resources.
Further, the specific method of step 2 is as follows:
2.1 Defining vectorsRepresenting the execution order of all tasks, collectionTo perform tasks->Is a combat unit set, i.e. combat mission->Wherein->To perform tasks->Is a battle sheet of (a)The number of elements is that the execution main bodies of all combat tasks are gathered intoThe solution of the combat task allocation problem is obtainedQ,S)；
2.2 According to the resource requirement of the fight task and the fight capability of the fight unit, determining the index of task allocation, and establishing the following objective function:
（1）
in the formula (1), the components are as follows,and->Two objective functions are: />Is the total duration of task completion, < >>Is task cost consumption; wherein (1)>The calculation formula of (2) is as follows:
（2）
in the formula (2), the amino acid sequence of the compound,for tasks->The execution end time of (2) is the total time length of the task completion from 0 as the initial point to the latest end time of the task execution,/>The calculation mode of (2) is as follows:
（3）
in the formula (3), the amino acid sequence of the compound,the calculation of the task starting time is the completion time of the corresponding combat unit for executing the precursor task ∈>Adding maneuvering time for the combat unit to travel from the previous task execution location to the current task execution location; />For tasks->Combat unitExecution queue->Corresponding task in->Queue for the task>Serial number of->Representing execution queuesMiddle->Is a precursor task of (1); />Representing combat units->Two adjacent combat tasks in the execution queue +.>And->Distance between, i.e.)>And->Distance between, when->In the time-course of which the first and second contact surfaces,representing combat units->From its initial position->Go to first task in execution queue +.>Is a distance between execution sites;
task cost consumption refers to the cost of the combat resource spent completing all tasks, calculated as follows:
（4）；
in the formula (4), the amino acid sequence of the compound,is a combat unit->Execution task->Weapon use coefficient of->Is the firstnWeapon resource costs of individual combat units;
2.3 Using the constraint of formulas (5) - (6) to constrain the task allocation scheme, the constraint being expressed as follows:
（5）
（6）
in the formula (5), the amino acid sequence of the compound,representation ofMIndividual combat mission, < >>Representing combat mission->Execution end time of->Is task->Is/are/is/are limited by the completion time requirement threshold value>Indicating that each task can be completed within its required completion time; wherein (1)>Is task->For the firstvDemand for item weapon resources;
in the formula (6), the amino acid sequence of the compound,representation ofVDifferent weapon resources are planted;
the weapon resource co-operation of all the fight units executing the same task is represented to meet the requirements of the task on various weapon resources.
Further, the specific method of step 3 is as follows:
(3.1) ordering by group
Setting a time unit SLOT, dividing the task into the following steps by taking the integral multiple of SLOT as a boundary point according to different deadline requirements of each combat taskgIndividual packetsAnd sorting the groups according to the execution time requirements of tasks in the groups, so that any three adjacent groups after sorting all meet the formula (7):
（7）
in the method, in the process of the invention,representing any three adjacent groups of tasks grouped after ordering,
in each case any task of the three groups, there must be +.>Wherein->Respectively, complete task->The deadline requirement of any combat task in the previous group is necessarily before the deadline of any combat task in the subsequent group, and the order of the combat tasks in the groups is arbitrary; the ordered groups are spliced together in sequence to obtain an initial task execution sequence +.>；
(3.2) capability requirement constraint handling
Each particle is based on the task execution order obtained by the group ordering strategy in (3.1)The position vectors are initialized in sequence, and the initialization method is as follows:
(3.2.1) selecting a task execution orderFirst task of (a)>；
(3.2.2) selecting a combat unit from the set of combat units that meets the requirements of the selected combat mission;
(3.2.3) determining whether the selected battle cell is in the execution subject of the selected taskIf not, adding the combat unit to the execution subject corresponding to the combat task>If not, skipping the combat unit to re-execute the step (3.2.2);
(3.2.4) judging the fight task execution subjectWhether the weapon resource aggregate involved satisfies the task +.>If not, executing the step (3.2.2);
(3.2.5) if the task requirements are satisfied, determining whether all tasks have been allocated, i.e., whetherIf not, selecting the next task to let +.>And executing the step (3.2.2), if the step is completed, indicating that the particle initialization is finished, and obtaining the set of execution subjects of all tasks>；
(3.3) the initialized particle individuals obtained in (3.1) and (3.2)The method adopts a two-dimensional matrix mode to code the particle individuals, and comprises the following specific steps:
(1) order of task executionSequentially filling each task in a first row of the two-dimensional matrix;
(2) for each combat task in the first row of the matrix, filling each unit of the execution body of the combat task in the corresponding position in the second row of the matrix, namely finishing the coding of single particles; the particle code comprises two lines of information, namely first behavior task number information, wherein each number represents a combat task; and the second behavior combat unit number information, wherein each number corresponds to one combat unit, and the same combat unit can appear at most once in the execution main body set of the same combat task.
Further, in step 4, each particle updates the state of the particle by means of cross-evolving with elite individuals, wherein the elite individuals are non-dominant individuals in the particle swarm under the current state, and the specific process is as follows:
(4.1) generating an empty particle individual as an updated new individual;
(4.2) setting the probability of evolution to elite individualsAnd local search probability->Selecting the first combat mission in elite individuals +.>；
(4.3) generating two random numbersAnd->If->And->The task is inserted into a corresponding position of a new individual after a local search strategy based on commonality is executed on the task; if->And->Copying the task in elite individuals and the corresponding execution individual set to the corresponding position in new individuals; if->No change is made, let +.>Repeating the step (4.3) until +.>；
(4.4) starting to traverse the combat task in the current individual, selecting the first task in the current individual；
(4.5) determining whether the assignment scheme for the task is already present in the new individual, and if so, selecting the next task orderAnd repeating step (4.5); if not, a random number is generated>If->The task is inserted into the first empty position of the new individual after the local search strategy based on the commonality is executed on the task; if->Directly copying the task in the current individual and the corresponding execution individual set to the first empty position in the new individual;
and (4.6) judging whether all tasks in the current individual are traversed, if not, selecting the next task in the current individual, and executing the step (4.5), and if so, indicating that the execution of the particle updating strategy based on the cross evolution is ended.
Further, in step (4.3), a local search strategy based on commonality is executed on the task, and the specific method is as follows:
at the present individualAnd elite individuals->Selecting task when cross updating>Respectively taking out the set of execution subjects of the task in two individuals +.>And->Taking the intersection of two sets +.>Judging->Whether or not to meet->If not, calculating the remaining required capacity requirement as task +.>Assigning new combat units to meet +.>And add newly allocated combat units to +.>In (a) and (b); otherwise, no change is neededExecution subject of (2) is changed to->And inserts it into the corresponding location of the new individual.
The beneficial effects are that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the collaborative multitask distribution method based on the improved particle swarm optimization comprehensively considers the diversified combat capability possessed by the combat unit, combat time consumption of task execution, weapon application cost and various constraint conditions, establishes a collaborative multitask distribution model, and compared with the conventional common task distribution algorithm, the collaborative multitask distribution method provided by the invention has the advantage that the same multitask distribution established by the invention is more fit with the actual problem; the improved particle swarm algorithm is adopted to solve the problem, the algorithm encodes each allocation scheme, and an initialization strategy based on group ordering, a particle updating strategy based on cross evolution and a local searching strategy based on commonality are adopted; the invention can quickly and accurately generate the combat scheme, is beneficial to the auxiliary decision of commanders and improves the combat benefit of combat troops.
Drawings
FIG. 1 is a flow chart of a task allocation algorithm of the present invention.
Fig. 2 is a flow chart of particle initialization of the present invention.
FIG. 3 is a schematic diagram of a particle update process based on cross evolution.
FIG. 4 is a schematic diagram of a commonality-based local search strategy of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
As shown in fig. 1, the present invention proposes a cooperative multitasking method based on an improved particle swarm algorithm, which comprises the following steps:
step 1, obtaining a cooperative battleSet of individual combat units->Set->Information of each combat unit is contained in the information; acquisition->Set of individual combat tasks->Set->Each combat task target information is contained in the system;
step 2, establishing a cooperative multitask allocation model for weapon resource requirements and weapon resources owned by a fight unit according to the fight task;
step 3, solving a cooperative multitasking distribution model by adopting an improved particle swarm optimization algorithm;
step 4, updating and iterating the initialized particle positions by adopting a particle updating strategy based on cross evolution and a local searching strategy based on commonality;
and 5, outputting all particle positions in the solution set after iteration is completed, namely, a task allocation scheme.
Further, the specific method of step 1 is as follows:
1.1 Assume that the decomposition of the combat target for the upper combat is provided withTask list of individual combat tasksDivided intoNPersonal combat unit->；
1.2 Each task is expressed as，/>Wherein->Is task->Is a target location of (2); />Is task->Is performed in the same manner as the execution time of the first step; />Is task->Is required for the completion time of (2); />Is task->Capability requirement vector of (1), and hasWherein->Is task->For the firstvDemand for item weapon resources;
1.3 Each combat unit is represented asWherein->Is a combat unit->Is>Is a combat unit->Is>Is a combat unit->Weapon resource costs of->Is a combat unit->Owned weapon resource vector, and hasWherein->Is a combat unitPossess->Number of weapon resources of item size,/->，/> Representation of/>Different weapon resources.
Further, the specific method of step 2 is as follows:
2.1 Defining vectorsRepresenting the execution order of all tasks, collectionTo perform tasks->Is a combat unit set, i.e. combat mission->Wherein->To perform tasks->The number of the combat units of (1) is that the execution subjects of all combat tasks are gathered intoThe solution of the combat task allocation problem is obtainedQ,S)；
2.2 According to the resource requirement of the fight task and the fight capability of the fight unit, determining the index of task allocation, and establishing the following objective function:
（1）
in the formula (1), the components are as follows,and->Two objective functions are: />Is the total duration of task completion, < >>Is task cost consumption; wherein (1)>The calculation formula of (2) is as follows:
（2）
in the formula (2), the amino acid sequence of the compound,for tasks->The execution end time of (2) is the total time length of the task completion from 0 as the initial point to the latest end time of the task execution,/>The calculation mode of (2) is as follows:
（3）
in the formula (3), the amino acid sequence of the compound,the calculation of the task starting time is the completion time of the corresponding combat unit for executing the precursor task ∈>Adding maneuvering time for the combat unit to travel from the previous task execution location to the current task execution location; />For tasks->Combat unitExecution queue->Corresponding task in->Queue for the task>Serial number of->Representing execution queuesMiddle->Is a precursor task of (1); />Representing combat units->Two adjacent combat tasks in the execution queue +.>And->Distance between, i.e.)>And->Distance between, when->In the time-course of which the first and second contact surfaces,representing combat units->From its initial position->Go to first task in execution queue +.>Is a distance between execution sites;
task cost consumption refers to the cost of the combat resource spent completing all tasks, calculated as follows:
（4）；
in the formula (4), the amino acid sequence of the compound,is a combat unit->Execution task->Weapon use coefficient of->Is the firstnWeapon resource costs of individual combat units;
2.3 Using the constraint of formulas (5) - (6) to constrain the task allocation scheme, the constraint being expressed as follows:
（5）
（6）
in the formula (5), the amino acid sequence of the compound,representation ofMIndividual combat mission, < >>Representing combat mission->Execution end time of->Is any one ofBusiness->Is/are/is/are limited by the completion time requirement threshold value>Indicating that each task can be completed within its required completion time; wherein (1)>Is task->For the firstvDemand for item weapon resources;
in the formula (6), the amino acid sequence of the compound,representation ofVThe different weapon resources are of a variety,the weapon resource co-operation of all the fight units executing the same task is represented to meet the requirements of the task on various weapon resources.
Further, the specific method of step 3 is as follows:
(3.1) ordering by group
Setting a time unit SLOT, dividing the task into the following steps by taking the integral multiple of SLOT as a boundary point according to different deadline requirements of each combat taskgIndividual packetsAnd sorting the groups according to the execution time requirements of tasks in the groups, so that any three adjacent groups after sorting all meet the formula (7):
（7）
in the method, in the process of the invention,representing any three adjacent groups of tasks grouped after ordering,
in each case any task of the three groups, there must be +.>Wherein->Respectively, complete task->The deadline requirement of any combat task in the previous group is necessarily before the deadline of any combat task in the subsequent group, and the order of the combat tasks in the groups is arbitrary; the ordered groups are spliced together in sequence to obtain an initial task execution sequence +.>；
(3.2) capability requirement constraint processing, the process is shown in fig. 2;
each particle is based on the task execution order obtained by the group ordering strategy in (3.1)The position vectors are initialized in sequence, and the initialization method is as follows:
(3.2.1) selecting a task execution orderFirst task of (a)>；
(3.2.2) selecting a combat unit from the set of combat units that meets the requirements of the selected combat mission;
(3.2.3) determining whether the selected battle cell is in the execution subject of the selected taskIf not, adding the combat unit to the execution subject corresponding to the combat task>If not, skipping the combat unit to re-execute the step (3.2.2);
(3.2.4) judging the fight task execution subjectWhether the weapon resource aggregate involved satisfies the task +.>If not, executing the step (3.2.2);
(3.2.5) if the task requirements are satisfied, determining whether all tasks have been allocated, i.e., whetherIf not, selecting the next task to let +.>And executing the step (3.2.2), if the step is completed, indicating that the particle initialization is finished, and obtaining the set of execution subjects of all tasks>；
(3.3) the initialized particle individuals obtained in (3.1) and (3.2)The method adopts a two-dimensional matrix mode to code the particle individuals, and comprises the following specific steps:
(1) order of task executionSequentially filling each task in a first row of the two-dimensional matrix;
(2) for each combat task in the first row of the matrix, filling each unit of the execution body of the combat task in the corresponding position in the second row of the matrix, namely finishing the coding of single particles; the particle code comprises two lines of information, namely first behavior task number information, wherein each number represents a combat task; and the second behavior combat unit number information, wherein each number corresponds to one combat unit, and the same combat unit can appear at most once in the execution main body set of the same combat task.
Further, as shown in fig. 3, in step 4, each particle updates the state of the particle by means of cross-evolving with elite individuals, wherein, elite individuals are non-dominant individuals in the particle swarm under the current state, the specific process is as follows:
(4.1) generating an empty particle individual as an updated new individual;
(4.2) setting the probability of evolution to elite individualsAnd local search probability->Selecting the first combat mission in elite individuals +.>；
(4.3) generating two random numbersAnd->If->And->The task is inserted into a corresponding position of a new individual after a local search strategy based on commonality is executed on the task; if->And->Copying the task in elite individuals and the corresponding execution individual set to the corresponding position in new individuals; if->No change is made, let +.>Repeating the step (4.3) until +.>；
(4.4) starting to traverse the combat task in the current individual, selecting the first task in the current individual；
(4.5) determining whether the assignment scheme for the task is already present in the new individual, and if so, selecting the next task orderAnd repeating step (4.5); if not, a random number is generated>If->The task is inserted into the first empty position of the new individual after the local search strategy based on the commonality is executed on the task; if->Directly copying the task in the current individual and the corresponding execution individual set to the first empty position in the new individual;
and (4.6) judging whether all tasks in the current individual are traversed, if not, selecting the next task in the current individual, and executing the step (4.5), and if so, indicating that the execution of the particle updating strategy based on the cross evolution is ended.
Further, in step (4.3), a local search strategy based on commonality is executed on the task, and the specific method is as follows:
at the present individualAnd elite individuals->Selecting task when cross updating>Respectively taking out the set of execution subjects of the task in two individuals +.>And->Taking the intersection of two sets +.>Judging->Whether or not to meet->If not, calculating the remaining required capacity requirement as task +.>Assigning new combat units to meet +.>And add newly allocated combat units to +.>In (a) and (b); otherwise, no change is neededExecution of (a)The main body is changed into->And inserts it into the corresponding location of the new individual.
Tasks numbered 18 are selected in FIG. 4Then, the set of execution subjects of the task in the two individuals is taken out respectively +.>And->Taking the intersection of two sets +.>Check->Whether or not to meet the task->Is found to be unsatisfactory, and the task is continuously allocated a combat unit and added to +.>Up to->Meet task->Is the capacity requirement of (1) at this time->Then, new individuals are treated with +.>Execution subject of (2) is changed to->。
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Claims (4)
1. A collaborative multitasking distribution method based on an improved particle swarm algorithm, characterized in that the method comprises the following steps:
step 1, obtaining a cooperative battleSet of individual combat units->Set->Information of each combat unit is contained in the information; acquisition->Set of individual combat tasks->Set->Each combat task target information is contained in the system;
step 2, establishing a cooperative multitask allocation model for weapon resource requirements and weapon resources owned by a fight unit according to the fight task;
step 3, solving a cooperative multitasking distribution model by adopting an improved particle swarm optimization algorithm;
step 4, updating and iterating the initialized particle positions by adopting a particle updating strategy based on cross evolution and a local searching strategy based on commonality;
step 5, after iteration is completed, outputting all particle positions in the solution set, namely, a task allocation scheme;
the specific method of the step 1 is as follows:
1.1 Assume that the decomposition of the combat target for the upper combat is provided withTask list of individual combat tasksDivided intoNPersonal combat unit->；
1.2 Each task is expressed as，/>Wherein, the method comprises the steps of, wherein,is task->Is a target location of (2); />Is task->Is performed in the same manner as the execution time of the first step; />Is task->Is required for the completion time of (2); />Is task->Capability requirement vector of (1), and hasWherein->Is task->For the firstvDemand for item weapon resources;
1.3 Each combat unit is represented asWherein->Is a combat unit->Is>Is a combat unit->Is>Is a combat unit->Weapon resource costs of->Is a combat unit->Owned weapon resource vector, and hasWherein->Is a combat unitPossess->Number of weapon resources of item size,/->，/> Representation of/>Different weapon resources are planted;
the specific method of the step 2 is as follows:
2.1 Defining vectorsRepresenting the execution order of all tasks, collectionTo perform tasks->Is a combat unit set, i.e. combat mission->Wherein->To perform tasks->The number of the combat units of (1) is that the execution subjects of all combat tasks are gathered intoThe solution of the combat task allocation problem is obtainedQ,S)；
2.2 According to the resource requirement of the fight task and the fight capability of the fight unit, determining the index of task allocation, and establishing the following objective function:
（1）
in the formula (1), the components are as follows,and->Two objective functions are: />Is the total duration of task completion, < >>Is task cost consumption; wherein (1)>The calculation formula of (2) is as follows:
（2）
in the formula (2), the amino acid sequence of the compound,for tasks->The execution end time of (2) is the total time length of the task completion from 0 as the initial point to the latest end time of the task execution,/>The calculation mode of (2) is as follows:
（3）
in the formula (3), the amino acid sequence of the compound,the calculation of the task starting time is the completion time of the corresponding combat unit for executing the precursor task ∈>Adding maneuvering time for the combat unit to travel from the previous task execution location to the current task execution location; />For tasks->In combat Unit->Execution queue->Corresponding task in->Queue for the task>Serial number of->Representing execution queue->In (a)Is a precursor task of (1); />Representing combat units->Two adjacent combat tasks in the execution queue of (a)And->Distance between, i.e.)>And->Distance between, when->When (I)>Representing combat units->From its initial position->Go to execution teamFirst task in column->Is a distance between execution sites;
task cost consumption refers to the cost of the combat resource spent completing all tasks, calculated as follows:
（4）；
in the formula (4), the amino acid sequence of the compound,is a combat unit->Execution task->Weapon use coefficient of->Is the firstnWeapon resource costs of individual combat units;
2.3 Using the constraint of formulas (5) - (6) to constrain the task allocation scheme, the constraint being expressed as follows:
（5）
（6）
in the formula (5), the amino acid sequence of the compound,representation ofMIndividual combat mission, < >>Representing combat mission->Execution end time of->Is task->Is/are/is/are limited by the completion time requirement threshold value>Indicating that each task can be completed within its required completion time; wherein (1)>Is task->For the firstvDemand for item weapon resources; in the formula (6), the amino acid sequence of the compound,representation ofVDifferent weapon resources, ++>The weapon resource co-operation of all the fight units executing the same task is represented to meet the requirements of the task on various weapon resources.
2. The method for collaborative multiplexing allocation based on improved particle swarm algorithm according to claim 1, wherein the specific method of step 3 is as follows:
(3.1) ordering by group
Setting a time unit SLOT, dividing the task into the following steps by taking the integral multiple of SLOT as a boundary point according to different deadline requirements of each combat taskgIndividual packetsAnd sorting the groups according to the execution time requirements of tasks in the groups, so that any three adjacent groups after sorting all meet the formula (7):
（7）
in the method, in the process of the invention,representing any three adjacent groups of tasks grouped after ordering,
in each case any task of the three groups, there must be +.>Wherein->Respectively, complete task->The deadline requirement of any combat task in the previous group is necessarily before the deadline of any combat task in the subsequent group, and the order of the combat tasks in the groups is arbitrary; the ordered groups are spliced together in sequence to obtain an initial task execution sequence +.>；
(3.2) capability requirement constraint handling
Each particle is based on the task execution order obtained by the group ordering strategy in (3.1)The position vectors are initialized in sequence, and the initialization method is as follows:
(3.2.1) selecting a task execution orderFirst task of (a)>；
(3.2.2) selecting a combat unit from the set of combat units that meets the requirements of the selected combat mission;
(3.2.3) determining whether the selected battle cell is in the execution subject of the selected taskIf not, adding the combat unit to the execution subject corresponding to the combat task>If not, skipping the combat unit to re-execute the step (3.2.2);
(3.2.4) judging the fight task execution subjectWhether the weapon resource aggregate involved satisfies the task +.>If not, executing the step (3.2.2);
(3.2.5) if the task requirements are satisfied, determining whether all tasks have been allocated, i.e., whetherIf not, selecting the next task to let +.>And executing the step (3.2.2), if the step is completed, indicating that the particle initialization is finished, and obtaining the set of execution subjects of all tasks>；
(3.3) the initialized particle individuals obtained in (3.1) and (3.2)The method adopts a two-dimensional matrix mode to code the particle individuals, and comprises the following specific steps:
(1) order of task executionSequentially filling each task in a first row of the two-dimensional matrix;
(2) for each combat task in the first row of the matrix, filling each unit of the execution body of the combat task in the corresponding position in the second row of the matrix, namely finishing the coding of single particles; the particle code comprises two lines of information, namely first behavior task number information, wherein each number represents a combat task; and the second behavior combat unit number information, wherein each number corresponds to one combat unit, and the same combat unit can appear at most once in the execution main body set of the same combat task.
3. The method for collaborative multiplexing distribution based on improved particle swarm algorithm according to claim 2, wherein in step 4, each particle updates the state of the particle by cross-evolving with elite individuals, wherein elite individuals are non-dominant individuals in the particle swarm in the current state, specifically comprising the following steps:
(4.1) generating an empty particle individual as an updated new individual;
(4.2) setting the probability of evolution to elite individualsAnd local search probability->Selecting the first combat mission in elite individuals +.>；
(4.3) generating two random numbersAnd->If->And->The task is inserted into a corresponding position of a new individual after a local search strategy based on commonality is executed on the task; if->And->Copying the task in elite individuals and the corresponding execution individual set to the corresponding position in new individuals; if->No change is made, letRepeating the step (4.3) until +.>；
(4.4) starting to traverse the combat task in the current individual, selecting the first task in the current individual；
(4.5) determining whether the assignment scheme of the task already existsIn the new individual, if present, select the next task orderAnd repeating step (4.5); if not, a random number is generated>If->The task is inserted into the first empty position of the new individual after the local search strategy based on the commonality is executed on the task; if->Directly copying the task in the current individual and the corresponding execution individual set to the first empty position in the new individual;
and (4.6) judging whether all tasks in the current individual are traversed, if not, selecting the next task in the current individual, and executing the step (4.5), and if so, indicating that the execution of the particle updating strategy based on the cross evolution is ended.
4. A collaborative multi-tasking method based on an improved particle swarm algorithm according to claim 3, wherein in step (4.3), a commonality-based local search strategy is performed for the task, specifically as follows:
at the present individualAnd elite individuals->Selecting task when cross updating>Respectively taking out the set of execution subjects of the task in two individuals +.>And->Taking the intersection of two sets +.>Judging->Whether or not to meetIf not, calculating the remaining required capacity requirement as task +.>Assigning new combat units to meet +.>And add newly allocated combat units to +.>In (a) and (b); otherwise, no modification is needed, +.>Execution subject of (2) is changed to->And inserts it into the corresponding location of the new individual.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410067031.2A CN117575299B (en) | 2024-01-17 | 2024-01-17 | Collaborative multitasking distribution method based on improved particle swarm algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410067031.2A CN117575299B (en) | 2024-01-17 | 2024-01-17 | Collaborative multitasking distribution method based on improved particle swarm algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117575299A CN117575299A (en) | 2024-02-20 |
| CN117575299B true CN117575299B (en) | 2024-04-02 |
Family
ID=89888623
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410067031.2A Active CN117575299B (en) | 2024-01-17 | 2024-01-17 | Collaborative multitasking distribution method based on improved particle swarm algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117575299B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113009934A (en) * | 2021-03-24 | 2021-06-22 | 西北工业大学 | Multi-unmanned aerial vehicle task dynamic allocation method based on improved particle swarm optimization |
| CN113792985A (en) * | 2021-08-23 | 2021-12-14 | 北京理工大学 | Sensor-weapon dynamic joint task multi-target allocation method |
| WO2022007376A1 (en) * | 2020-07-08 | 2022-01-13 | 北京航空航天大学 | Multi-objective multimodal particle swarm optimization method based on bayesian adaptive resonance |
| WO2022057811A1 (en) * | 2020-09-17 | 2022-03-24 | 浙江大学 | Edge server-oriented network burst load evacuation method |
| CN116681223A (en) * | 2023-04-17 | 2023-09-01 | 北京理工大学 | Multi-stage combat resource collaborative allocation method based on enhanced MOEA/D |
-
2024
- 2024-01-17 CN CN202410067031.2A patent/CN117575299B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022007376A1 (en) * | 2020-07-08 | 2022-01-13 | 北京航空航天大学 | Multi-objective multimodal particle swarm optimization method based on bayesian adaptive resonance |
| WO2022057811A1 (en) * | 2020-09-17 | 2022-03-24 | 浙江大学 | Edge server-oriented network burst load evacuation method |
| CN113009934A (en) * | 2021-03-24 | 2021-06-22 | 西北工业大学 | Multi-unmanned aerial vehicle task dynamic allocation method based on improved particle swarm optimization |
| CN113792985A (en) * | 2021-08-23 | 2021-12-14 | 北京理工大学 | Sensor-weapon dynamic joint task multi-target allocation method |
| CN116681223A (en) * | 2023-04-17 | 2023-09-01 | 北京理工大学 | Multi-stage combat resource collaborative allocation method based on enhanced MOEA/D |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117575299A (en) | 2024-02-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106990792B (en) | Multi-unmanned aerial vehicle collaborative time sequence coupling task allocation method based on hybrid gravity search algorithm | |
| CN107515618B (en) | Heterogeneous unmanned aerial vehicle cooperative task allocation method considering time window | |
| CN105302153B (en) | The planing method for the task of beating is examined in the collaboration of isomery multiple no-manned plane | |
| CN114442662B (en) | Unmanned plane cluster cooperative ground striking method based on improved wolf's group optimization algorithm | |
| CN103279793B (en) | A kind of unmanned vehicle formation method for allocating tasks determined under environment | |
| Ye et al. | Cooperative multiple task assignment of heterogeneous UAVs using a modified genetic algorithm with multi-type-gene chromosome encoding strategy | |
| CN111240353A (en) | Unmanned aerial vehicle collaborative air combat decision method based on genetic fuzzy tree | |
| CN113962012B (en) | Unmanned aerial vehicle countermeasure strategy optimization method and device | |
| CN111813144B (en) | Multi-unmanned aerial vehicle collaborative route planning method based on improved flocks of sheep algorithm | |
| CN112633654A (en) | Multi-unmanned aerial vehicle task allocation method based on improved cluster expansion consistency bundle algorithm | |
| CN113031650B (en) | Unmanned aerial vehicle cluster cooperative target distribution design method under uncertain environment | |
| CN113485456A (en) | Distributed online self-adaptive task planning method for unmanned aerial vehicle group | |
| CN114417735A (en) | Multi-unmanned aerial vehicle cooperative task planning method in cross-regional combined combat | |
| CN112801540A (en) | Intelligent cooperative architecture design based on unmanned cluster | |
| CN114326827B (en) | Unmanned aerial vehicle cluster multitasking dynamic allocation method and system | |
| CN114142908A (en) | Multi-unmanned aerial vehicle communication resource allocation method for coverage reconnaissance task | |
| CN111797966B (en) | Multi-machine collaborative global target distribution method based on improved flock algorithm | |
| CN117575299B (en) | Collaborative multitasking distribution method based on improved particle swarm algorithm | |
| CN113608546A (en) | Quantum sea lion mechanism unmanned aerial vehicle group task allocation method | |
| Jing et al. | Cooperative task assignment for heterogeneous multi-UAVs based on differential evolution algorithm | |
| CN115564374A (en) | Collaborative multitask redistribution method, device, equipment and readable storage medium | |
| CN116126493A (en) | SA-GA-based heterogeneous multi-star collaborative task planning method | |
| CN115220473A (en) | Multi-unmanned aerial vehicle swarm cooperative task dynamic allocation method | |
| CN115034557A (en) | Agile satellite emergency task planning method | |
| Su et al. | Multi-UAVs target attack based on improved genetic algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |