摘要
针对传统的粒子群算法与遗传算法在解决武器目标分配优化时存在收敛精度不高的问题,提出了一种基于粒子群遗传禁忌的武器目标分配优化算法WTAO-PGT。通过引入自适应选择比例调控种群多样性,并改进遗传算法的交叉、变异算子以及禁忌搜索的邻域动作,使算法具有较强跳出局部极值的能力。仿真实验结果表明,所提算法在收敛精度上较粒子群禁忌混合搜索等算法有较大提升。
Aiming at the problem of low convergence accuracy of traditional particle swarm optimization and genetic algorithm in solving weapon target assignment optimization,the weapon target assignment optimization algorithm WTAO-PGT was proposed,which was based on particle swarm genetic taboo.The self-adaptive selection ratio to control the population diversity was introduced,and the crossover and mutation operators of the genetic algorithm and the neighborhood action of the taboo search were improved.Therefore,the strong ability to jump out of the local extreme values was possessed by the algorithm.Compared with other algorithms such as particle taboo hybrid search,the proposed algorithm greatly improved in convergence accuracy as shown by the simulation results.
作者
佘维
牛文涛
孔德锋
田钊
SHE Wei;NIU Wentao;KONG Defeng;TIAN Zhao(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China;Zhengzhou Key Laboratory of Blockchain and Data Intelligence,Zhengzhou 450002,China;Institute of Engineering Protection,Institute of Defense Engineering,Academy of Military Sciences,Luoyang 471023,China)
出处
《郑州大学学报（理学版）》
CAS
北大核心
2023年第5期1-10,共10页
Journal of Zhengzhou University:Natural Science Edition
基金
河南省重点研发与推广专项(212102310039)
河南省高等学校重点科研项目(20A520035)。
关键词
武器目标分配
粒子群算法
遗传算法
禁忌搜索
自适应选择比例
邻域动作
weapon target assignment
particle swarm algorithm
genetic algorithm
taboo search
selfadaptive selection ratio
neighborhood action
