Multi-strategy improved distributed cross-domain collaborative task allocation optimization method

Abstract

The invention provides a multi-strategy improved distributed cross-domain cooperative task allocation optimization method, and belongs to the technical field of planning control. The method comprises the following steps: firstly, constructing a target allocation optimization model based on three types of target functions of comprehensive efficiency, time cost and resource utilization rate; a control distribution optimization model is constructed based on three objective functions of sensor detection capability, sending link connectivity and return link connectivity; and then combining the two models and corresponding constraint conditions to construct a cross-domain collaborative task allocation optimization model. And finally, carrying out distributed cross-domain collaborative task optimization allocation calculation by adopting a multi-strategy improved particle swarm optimization algorithm. According to the multi-strategy improved distributed cross-domain cooperative task allocation optimization method, the overall combat effectiveness is improved, the concealment and the collaboration among combat nodes are enhanced, powerful technical support is provided for cross-domain cooperative combat, and the task execution efficiency and the resource utilization benefit can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of planning and control technology, and in particular to a multi-strategy improved distributed cross-domain collaborative task allocation optimization method. Background Technology

[0002] Cross-domain collaborative task allocation involves the transfer of control and relay control among nodes in multiple domains, including land, sea, and air. It involves optimizing the allocation of complex tasks by comprehensively utilizing internal cluster resources based on shared situational awareness information.

[0003] Current task allocation optimization methods primarily involve constructing a function with execution efficiency as the objective, setting constraints, and implementing penalty functions to achieve the optimization goal. In terms of optimization methods, these can be categorized into traditional exact algorithms, heuristic algorithms, and hybrid intelligent algorithms that combine multiple intelligent algorithms.

[0004] For example, in the 2023 journal article "An Optimization Algorithm for Weapon Target Allocation Based on Particle Swarm Optimization and Genetic Taboos" published in the Journal of Zhengzhou University (Science Edition), an adaptive selection ratio is introduced to regulate population diversity, and the crossover and mutation operators of the genetic algorithm, as well as the neighborhood action of taboo search, are improved to enhance the convergence accuracy of the optimization algorithm. This method only addresses the optimization allocation problem between ammunition and weapons, has a relatively simple task type, and does not involve cross-domain cooperative combat problems.

[0005] As in Example 2, the 2022 Aeronaut journal paper "Multitarget allocation strategy based on adaptive SA-PSO algorithm" combines the advantages of simulated annealing and particle swarm optimization algorithms to propose a multitarget optimal allocation strategy. This method is mainly aimed at the cooperative interception mission of multiple ground targets and does not involve cross-domain air-sea cooperative operations.

[0006] As in Example 3, the 2024 journal paper "Path Planning for a Transport Robot Based on an Improved Dung Beetle Optimization Algorithm" in Modern Manufacturing Engineering uses a heuristic swarm intelligence search algorithm to implement the robot path planning task. This method mainly focuses on optimizing the path planning problem of small-scale unmanned swarms in a limited space, and does not involve complex tasks with multiple objectives and types or cross-domain cooperative combat problems.

[0007] Due to the complex constraints, large scale, and high time sensitivity of cross-domain collaborative task allocation, the aforementioned optimization algorithms have not provided a better solution for the problem of unified allocation of multiple task objectives. Therefore, this invention proposes a multi-strategy improved distributed cross-domain collaborative task allocation method to address the shortcomings of the existing technologies. Summary of the Invention

[0008] The purpose of this invention is to provide a multi-strategy improved distributed cross-domain collaborative task allocation optimization method, which not only improves the overall combat effectiveness, but also enhances the concealment and collaboration between combat nodes, providing strong technical support for cross-domain collaborative operations, and can effectively improve task execution efficiency and resource utilization.

[0009] To achieve the above objectives, this invention provides a multi-strategy improved method for optimizing distributed cross-domain collaborative task allocation, comprising the following steps: Step S1: Based on three types of objective functions—comprehensive efficiency, time cost, and resource utilization rate—construct an objective allocation optimization model; Step S2: Based on three types of objective functions—sensor detection capability, transmission link connectivity, and return link connectivity—construct a control allocation optimization model; Step S3: Combine the target allocation optimization model and the control allocation optimization model, along with the corresponding constraints, to construct a cross-domain collaborative task allocation optimization model; Step S4: Use a multi-strategy improved particle swarm optimization algorithm to perform distributed cross-domain collaborative task optimization and allocation calculation; Step S41: Use the improved Fuch chaotic mapping algorithm to generate an initial population to increase the balance of individual distribution; Step S42: Based on the decision number P, control whether to use the isolated point reverse evolution algorithm for population update in order to expand the search space; Step S43: Use the optimal individual local perturbation evolution algorithm to update the population, so as to increase the individual difference information and avoid getting trapped in local optima; Step S44: Update the individual optimal and global optimal based on the PSO algorithm, and update the particle velocity and position; Step S45: Iterate through steps S41 to S44 until the iteration termination criterion is met.

[0010] Preferably, in step S1, an objective allocation optimization model is constructed based on three types of objective functions: comprehensive efficiency, time cost, and resource utilization rate, including: (1); (2); in, This represents the objective function of the objective allocation optimization model; This indicates overall effectiveness, including the level of threat to the target. Target Value Probability of target being destroyed ; Indicates time cost, including target distance. Cluster movement speed ; This indicates resource utilization rate, including the actual amount of resources allocated to the target. Target available allocation of resources ; , , All represent weighting coefficients; Formula (2) is the operational domain node Allocation Target j The task constraints; where the first row represents the operational domain nodes of all assigned targets. The total number shall not exceed the total number of nodes in all combat domains. d The second line represents each combat domain node. Only map associated with one target j The third line represents each target. j Maximum associative mappings m Nodes Quantity; the fourth row represents the combat domain nodes. With the goal j Distance between Not exceeding the maximum theoretical range ; This function represents the target-operational domain node mapping relationship.

[0011] Preferably, in step S2, a control allocation optimization model is constructed based on three types of objective functions: sensor detection capability, transmission link connectivity, and return link connectivity, including: (3); (4); in, This represents the objective function of the control allocation optimization model; This indicates the sensor's detection capability, including the total number of targets identified. q Total number of nodes in all combat domains d ; Indicates transmission link connectivity, including operational domain nodes. Total received power ; Indicates backhaul link connectivity, including air relay nodes. Total received power ; Represents the connectivity function of the operational domain node-air relay node link; Formula (4) is for air relay nodes Assign control to operational domain nodes The constraints; where the first row represents all operational domain nodes assigned control. The total number shall not exceed the total number of nodes in all combat domains. dThe second line represents each combat domain node. Only one air relay node is mapped. The third line represents each air relay node. k Maximum associative mappings Each combat domain node quantity.

[0012] Preferably, in step S3, a cross-domain collaborative task allocation optimization model is constructed by combining the target allocation optimization model and the control allocation optimization model, along with the corresponding constraints, including: (1); (3); (5).

[0013] Preferably, in step S41, an improved Fuch chaotic mapping algorithm is used to generate an initial population to increase the balance of individual distribution, including: (6); in, Indicates the initial position of the population; This represents the population position after the improved Fuch chaotic mapping.

[0014] Preferably, in step S42, the decision number P controls whether to use the isolated point reverse evolution algorithm for population update to expand the search space, including: (7); in, Indicates the first t +1 iterations of isolated points; Indicates the first t The isolated point in the next iteration; Indicates the maximum extreme value of an individual; Represents the minimum extreme value of an individual; This represents the disturbance factor.

[0015] Preferably, in step S43, the population is updated using the optimal individual local perturbation evolution algorithm to increase individual difference information and avoid getting trapped in local optima, including: (8); (9); in, Indicates the first t +1 global optimal solution; Indicates the first t Suboptimal global solution; Indicates the local worst solution; Indicates the worst-case global solution; Indicates the control coefficient; , These represent the maximum and minimum values ​​of the control coefficient, respectively. Indicates no more than 10 -4 The constant; This represents the fitness function of an individual particle.

[0016] Therefore, the present invention employs the above-mentioned multi-strategy improved distributed cross-domain collaborative task allocation optimization method, and the beneficial technical effects are as follows: (1) Construction of large-scale complex mission model: This model combines the multi-mission target allocation with the collaborative control between various combat domain nodes through correlation analysis and organic integration. It accurately describes the multi-mission optimization allocation problem in complex mission scenarios, maximizes the protection of the concealment and collaboration between cross-domain combat nodes, and improves the overall combat effectiveness.

[0017] (2) Multi-strategy improved algorithm proposed: It combines multiple optimization algorithm improvement strategies, takes into account multiple constraints, accelerates the convergence of the algorithm, avoids getting trapped in local optima, and can be effectively applied to various cross-domain collaborative task allocation scenarios.

[0018] (3) Distributed planning structure design: Distributed task planning improves the autonomous decision-making ability of each combat node, overcomes the limitations of centralized decision-making such as high communication requirements, high computational complexity, low fault tolerance and poor system scalability, and fully mobilizes all available resources for efficient task allocation. Attached Figure Description

[0019] Figure 1 This is a flowchart of a multi-strategy improved distributed cross-domain collaborative task allocation optimization method according to the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0023] Example 1 like Figure 1 The diagram shows a flowchart of a multi-strategy improved distributed cross-domain collaborative task allocation optimization method according to the present invention, which includes the following steps: 1. Target allocation optimization model.

[0024] To address the problem of optimizing the allocation of multiple task objectives, three types of indicators—comprehensive effectiveness, time cost, and resource utilization—can be used for evaluation and measurement. For the combined optimization of these three types of indicators, an objective allocation optimization model is constructed. First, a normalization method is used to process the three types of objective functions, unifying the data scale and improving the convergence speed. Then, the Analytic Hierarchy Process (AHP) is used to perform a weighted calculation of the values ​​of the three types of objective functions—comprehensive effectiveness, time cost, and resource utilization. The specific optimization model and its constraints are shown in the following equation: (1); (2); in, This represents the objective function of the objective allocation optimization model; This indicates overall effectiveness, including the level of threat to the target. Target Value Probability of target being destroyed ; Indicates time cost, including target distance. Cluster movement speed ; This indicates resource utilization rate, including the actual amount of resources allocated to the target. Target available allocation of resources ; , , All represent weighting coefficients; Formula (2) is the operational domain node Allocation Target j The task constraints; where the first row represents the operational domain nodes of all assigned targets. The total number shall not exceed the total number of nodes in all combat domains. d The second line represents each combat domain node. Only map associated with one target j The third line represents each target. j Maximum associative mappings m Nodes Quantity; the fourth row represents the combat domain nodes. With the goal j Distance between Not exceeding the maximum theoretical range ; This function represents the target-operational domain node mapping relationship.

[0025] 2. Control allocation optimization model.

[0026] In cross-domain collaborative operations, command and control may be transferred or switched according to the surrounding situation or mission requirements. The specific switching times are as follows: 1) When the land / sea combat node reaches the control switching distance, the air relay node can transfer control to the above node; 2) When the land / sea combat node receives a serious threat or signal interference, control is transferred to the air relay node; 3) When a new combat target appears or a sudden change occurs, control is transferred to the air relay node.

[0027] To optimize the allocation of command and control authority to operational domain nodes, three indicators can be used for evaluation: sensor detection capability, transmit link connectivity, and return link connectivity. For the optimization problem involving a combination of these three indicators, a control allocation optimization model is constructed. The specific optimization model and its constraints are shown in the following equation: (3); (4); in, This represents the objective function of the control allocation optimization model; This indicates the sensor's detection capability, including the total number of targets identified. q Total number of nodes in all combat domains d ; Indicates transmission link connectivity, including operational domain nodes. Total received power ; Indicates backhaul link connectivity, including air relay nodes. Total received power ; Represents the connectivity function of the operational domain node-air relay node link; Formula (4) is for air relay nodes Assign control to operational domain nodes The constraints; where the first row represents all operational domain nodes assigned control. The total number shall not exceed the total number of nodes in all combat domains. d The second line represents each combat domain node. Only one air relay node is mapped. The third line represents each air relay node. Maximum associative mappings Each combat domain node quantity.

[0028] 3. Cross-domain collaborative task allocation optimization model.

[0029] For the cross-domain collaborative task allocation problem, taking the target allocation optimization result as a prerequisite, the control allocation optimization model is solved to obtain the cross-domain collaborative task allocation optimization scheme. The corresponding optimization model and its constraints are a combination of the two types of algorithms mentioned above, as shown in the following formula: (1); (3); (5).

[0030] 4. A multi-strategy improved distributed cross-domain collaborative task allocation optimization algorithm.

[0031] 1) PSO algorithm.

[0032] Particle Swarm Optimization (PSO) is a swarm intelligence random search algorithm that mimics bird predation. It searches the search space, recording the globally optimal position and velocity found in each iteration, and uses a fitness objective function to determine whether to update the current optimal position and velocity. This involves integrating the nodes in the combat domain. Considered as particles in the particle swarm optimization algorithm The algorithm updates the formula as follows: (6); (7); in, Indicates the first t +1 iteration particles speed; Indicates the first t Sub-iteration particles speed; Indicates the first t +1 iteration particles Location; Indicates the first t Sub-iteration particles Location; Indicates the process t +1 search iterations for particles The historical best position; Indicates the process t +1 search iterations to find the global optimal position of the entire population; This represents the inertial weight, which reflects the influence of the velocity at the previous moment on the next movement. , Indicates the learning factor; This represents the disturbance factor.

[0033] 2) Improved Fuch chaotic mapping algorithm.

[0034] Since the initial population has a significant impact on the overall optimization result, an improved Fuchs chaotic mapping algorithm is used to increase the distribution balance of individuals and prevent premature convergence. The algorithm formula is as follows: (8); in, Indicates the initial position of the population; This represents the population position after the improved Fuch chaotic mapping.

[0035] 3) Isolated point reverse evolution algorithm.

[0036] To further expand the search space of the algorithm, an isolated individual reverse evolution algorithm is proposed, which mainly performs reverse iterative evolution on isolated individuals outside the population. The update formula of the algorithm is as follows: (9); in, Indicates the first t +1 iterations of isolated points; Indicates the first t The isolated point in the next iteration; Indicates the maximum extreme value of an individual; This represents the minimum extreme value of an individual.

[0037] 4) Optimal individual local perturbation evolution algorithm.

[0038] To enhance individual diversity and avoid getting trapped in local optima during the optimization process, an optimal individual local perturbation evolutionary algorithm is proposed. This algorithm primarily introduces a control coefficient factor to adaptively add local perturbations to the optimal individual, thereby obtaining an updated global optimal solution.

[0039] (10); (11); in, Indicates the first t +1 global optimal solution; Indicates the first t Suboptimal global solution; Indicates the local worst solution; Indicates the worst-case global solution; Indicates the control coefficient; , These represent the maximum and minimum values ​​of the control coefficient, respectively. Indicates no more than 10 -4 The constant; This represents the fitness function of an individual particle.

[0040] 5) Improved Particle Swarm Optimization (PSO) Algorithm Flow with Multi-Strategy.

[0041] To achieve optimized allocation of distributed cross-domain collaborative tasks, a multi-strategy improvement to the particle swarm optimization algorithm is implemented to enhance the solution speed and accuracy for complex problems. First, an improved Fuch chaotic mapping algorithm is introduced to generate the initial population, reducing uneven population distribution and excessive individual differences. Then, a probability control strategy is adopted, introducing a decision number P to control the reverse evolution algorithm that enters extreme points, thereby expanding the population search space. Finally, an optimal individual local perturbation evolution algorithm is used to increase individual difference information and avoid getting trapped in local optima.

[0042] The specific steps of the algorithm are as follows: Step 1: Set algorithm parameters to improve overall performance Time cost Resource utilization rate Sensor detection capabilities Transmission link connectivity Backhaul link connectivity And set the upper and lower bounds of the parameters according to the constraints of formula (5); Step 2: According to formula (8), the initial population is processed based on the Fuchs chaotic mapping algorithm. Perform equalization processing; Step 3: Construct two types of objective optimization functions based on formulas (1) and (3), and calculate the fitness value of each particle. ; Step 4: If rand (0, 1) ≥ random decision number P (P is the number of iterations) t If the variable function increases and decreases, the algorithm gets stuck in a local optimum and proceeds to step 5; otherwise, it jumps to step 7. Step 5: According to formula (9), use the isolated point reverse evolution algorithm to find isolated points and perform population update; Step 6: According to formulas (10) and (11), the optimal individual local perturbation evolution algorithm is used to obtain the current global optimal solution. To perform population renewal; Step 7: Update the individual optimality based on the PSO algorithm Global Optimum ; Step 8: Update the particle velocity according to formulas (6) and (7). ,Location ; Step 9: Determine if the iteration termination criterion is met. If the number of iterations reaches the maximum number of iterations, the algorithm stops; otherwise, return to step 3.

[0043] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0044] Therefore, the above-mentioned multi-strategy improved distributed cross-domain collaborative task allocation optimization method not only improves the overall combat effectiveness, but also enhances the concealment and collaboration between combat nodes, providing strong technical support for cross-domain collaborative operations and effectively improving task execution efficiency and resource utilization benefits.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-strategy improved distributed cross-domain collaborative task allocation optimization method, characterized in that: The following steps are involved: Step S1, constructing a target allocation optimization model based on three types of objective functions: comprehensive efficiency, time cost and resource utilization; Step S2: construct a control allocation optimization model based on three objective functions: sensor detection capability, transmission link connectivity, and return link connectivity; Step S3: Combining the target allocation optimization model and the control allocation optimization model, as well as corresponding constraints, to construct a cross-domain collaborative task allocation optimization model; Step S4, using a multi-strategy improved particle swarm algorithm to perform distributed cross-domain collaborative task optimization allocation calculation; Step S41, using the improved Fuch chaotic mapping algorithm to generate an initialization population; Step S42, controlling whether to adopt the isolated point reverse evolution algorithm to update the population according to the decision number P; Step S43, using the optimal individual local perturbation evolutionary algorithm to update the population; Step S44: Update individual optimum and global optimum based on PSO algorithm, and update particle speed and position; Step S45, iteratively execute steps S41 to S44 until the iteration termination criterion is met.

2. According to claim 1, a multi-strategy improved distributed cross-domain collaborative task allocation optimization method is characterized in that: In step S1, a target allocation optimization model is constructed based on three objective functions: comprehensive efficiency, time cost, and resource utilization, including: （1）； （2）； in, represents the objective function of the target allocation optimization model; Indicates overall effectiveness, including target threat level , target value Probability of target being destroyed ; Indicates the time cost, including the target distance , Cluster movement speed ; Indicates resource utilization, including the actual number of resources allocated to the target , Target available allocation resource quantity ; , , All represent weighting coefficients; Formula (2) is the combat domain node Assignment Target j The first row represents the combat domain nodes of all assigned targets. The total number does not exceed the total number of nodes in all combat domains d ; The second line represents each combat domain node Mapping associations to only one target j ; The third line represents each target j Maximum possible mappings m Nodes Quantity; the fourth line represents the combat domain node With the goal j The distance between Not exceeding the maximum theoretical range ; Represents the target-combat domain node mapping relationship function.

3. According to claim 2, a multi-strategy improved distributed cross-domain collaborative task allocation optimization method is characterized in that: In step S2, based on three objective functions, namely, sensor detection capability, transmission link connectivity, and return link connectivity, a control allocation optimization model is constructed, including: （3）； （4）； in, represents the objective function of the control allocation optimization model; Indicates the sensor detection capability, including the total number of target identifications q , Total number of nodes in all combat domains d ; Indicates the connectivity of the sending link, including the combat domain node Total received power ; Indicates the connectivity of the backhaul link, including the aerial relay nodes Total received power ; Represents the connectivity function of the combat domain node-air relay node link; Formula (4) is the aerial relay node Allocate control to combat domain nodes The first row represents all the combat domain nodes that are assigned control rights. The total number does not exceed the total number of nodes in all combat domains d ; The second line represents each combat domain node Only one aerial relay node is associated with the mapping ; The third row represents each aerial relay node Maximum possible mappings Operational Domain Node quantity.

4. According to claim 3, a multi-strategy improved distributed cross-domain collaborative task allocation optimization method is characterized in that: In step S3, a cross-domain collaborative task allocation optimization model is constructed by combining the target allocation optimization model and the control allocation optimization model, as well as corresponding constraints, including: （1）； （3）； （5）。 5. According to the multi-strategy improved distributed cross-domain collaborative task allocation optimization method of claim 1, it is characterized in that: In step S41, an improved Fuch chaotic mapping algorithm is used to generate an initialization population, including: （6）； in, represents the initial position of the population; Represents the population position after the improved Fuch chaotic map.

6. The multi-strategy improved distributed cross-domain collaborative task allocation optimization method according to claim 1 is characterized in that: In step S42, whether to use the isolated point reverse evolution algorithm to update the population is controlled according to the decision number P, including: （7）； in, Indicates t +1 iteration of isolated points; Indicates t The isolated point of the iteration; represents the individual maximum extreme value; represents the individual minimum extreme value; represents the disturbance factor.

7. The multi-strategy improved distributed cross-domain collaborative task allocation optimization method according to claim 1 is characterized in that: In step S43, the population is updated using the optimal individual local perturbation evolutionary algorithm, including: （8）； （9）； in, Indicates t +1 global optimal solution; Indicates t Sub-global optimal solution; represents the local worst solution; represents the global worst solution; represents the control coefficient; , Respectively represent the maximum and minimum values ​​of the control coefficient; Indicates no more than 10 -4 The constant of Represents the individual fitness function of particles.

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