An Adaptive-parallel Genetic Algorithm Applying for Weapon-target Assignment

doi:10.3901/JME.260400

Abstract

Abstract: The weapon-target assignment (WTA) for the air defense of strategic point is a typical multi-objective optimization problem, of which the purpose is to generate a reasonable and effective firepower allocation scheme to guide the defensive operations. A WTA model containing a bi-objective function of the remaining threat value of the incoming enemy target and the weapon cost is built. To solve the model quickly for real-time application, the improved adaptive-parallel non-dominated sorting genetic algorithm is proposed. Firstly, a vector sorting algorithm is introduced to improve the fast non-dominated sorting and reduce the computational complexity. Secondly, a truncated mean-based adaptive law is designed for crossover and variational operations to improve the convergence of the algorithm. Thirdly, the optimization is parallelized with an adaptive migration strategy to further reduce the solving time. Six simulation scenarios are constructed in the experiments to validate the proposed algorithm.

Key words: weapon-target assignment, multi-objective optimization, adaptive operator, parallel computation, NSGA-III

CLC Number:

E917

LIU Fuqiang, ZHOU Lun, LIU Zhongyang, CHU Zhigang, PU Huayan, LUO Jun. An Adaptive-parallel Genetic Algorithm Applying for Weapon-target Assignment[J]. Journal of Mechanical Engineering, 2026, 62(11): 116-131.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260400

http://www.cjmenet.com.cn/EN/Y2026/V62/I11/116

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