Constraint-Feature-Guided Evolutionary Algorithms for Multi-Objective Multi-Stage Weapon-Target Assignment Problems
The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements,...
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| Vydáno v: | Journal of systems science and complexity Ročník 38; číslo 3; s. 972 - 999 |
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| Jazyk: | angličtina |
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Springer Berlin Heidelberg
01.06.2025
Springer Nature B.V |
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| ISSN: | 1009-6124, 1559-7067 |
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| Abstract | The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment (CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms (CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks (i.e., NSGA-II, NSGA-III, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover, mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover, a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-II among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-II. |
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| AbstractList | The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment (CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms (CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks (i.e., NSGA-II, NSGA-III, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover, mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover, a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-II among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-II. |
| Author | Xin, Bin Wang, Yipeng Wang, Xianpeng Wang, Danjing Deng, Fang Zhang, Jia |
| Author_xml | – sequence: 1 givenname: Danjing surname: Wang fullname: Wang, Danjing organization: School of Automation, Beijing Institute of Technology, National Key Laboratory of Autonomous Intelligent Unmanned Systems – sequence: 2 givenname: Bin surname: Xin fullname: Xin, Bin email: brucebin@bit.edu.cn organization: School of Automation, Beijing Institute of Technology, National Key Laboratory of Autonomous Intelligent Unmanned Systems – sequence: 3 givenname: Yipeng surname: Wang fullname: Wang, Yipeng organization: School of Automation, Beijing Institute of Technology, National Key Laboratory of Autonomous Intelligent Unmanned Systems – sequence: 4 givenname: Jia surname: Zhang fullname: Zhang, Jia organization: School of Automation, Beijing Institute of Technology, National Key Laboratory of Autonomous Intelligent Unmanned Systems – sequence: 5 givenname: Fang surname: Deng fullname: Deng, Fang organization: School of Automation, Beijing Institute of Technology, National Key Laboratory of Autonomous Intelligent Unmanned Systems – sequence: 6 givenname: Xianpeng surname: Wang fullname: Wang, Xianpeng organization: National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University |
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| SubjectTerms | Command and control Complex Systems Constraints Control Effectiveness Evolutionary algorithms Genetic algorithms Heuristic methods Mathematics Mathematics and Statistics Mathematics of Computing Multiple objective analysis Operations Research/Decision Theory Random sampling Statistics Systems Theory Weapons |
| Title | Constraint-Feature-Guided Evolutionary Algorithms for Multi-Objective Multi-Stage Weapon-Target Assignment Problems |
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