基于遗传蚁群算法的武器目标分配优化方法  

Weapon Target Assignment Optimization Method Based on Genetic-Ant Colony Optimization

作　　者：翁年凤 刘艺 郑奇斌 段伟伟 刘坤 任小广 WENG Nianfeng;LIU Yi;ZHENG Qibin;DUAN Weiwei;LIU Kun;REN Xiaoguang(The Sixty-third Research Institute,National University of Defense Technology,Nanjing Jiangsu 210007,China;Academy of Military Sciences,Beijing 100091,China;Joint Operations College,National Defense University,Shijiazhuang Hebei 050000,China)

机构地区：[1]国防科技大学第六十三研究所,江苏南京210007 [2]军事科学院,北京100091 [3]国防大学联合作战学院,河北石家庄050000

出　　处：《海军航空大学学报》2024年第5期640-648,共9页Journal of Naval Aviation University

基　　金：国家自然科学青年基金(72201275);第八届青托工程(2022QNRC001)。

摘　　要：针对演化算法求解武器目标分配求解精度不高的问题,提出了结合蚁群优化的遗传蚁群算法。使用混沌映射随机初始化方法,通过逻辑混沌映射初始化种群,提升初始种群的多样性;通过多样性交换策略,交换染色体部分区域值,进一步提高算法的多样性;基于蚁群优化思想,提出蚁群重组准则,利用蚂蚁的搜索行为增强算法的收敛性。使用12个测试用例进行消融实验,证明了各策略的有效性;与7种典型方法进行对比实验,验证了所提方法的优越性。In order to tackle the low accuracy issue of evolutionary algorithms in solving weapon target assignment problems,an algorithm with ant colony optimization named Genetic Algorithm based on Ant Colony Optimization is proposed.Chaotic mapping random initialization method is developed to initialize the population through logical chaotic mapping to improve the diversity of the initial population.A diversity swap strategy is put forward to swap some chromosome regionsvalues to further improve the algorithms diversity.Based on the idea of ant colony optimization,the ant colony recombination criterion is raised to enhance the algorithms convergence by using the ants search behavior.12 test instances are used for experiments,the ablation experiment proves the effectiveness of each strategy,and the comparative experiment results with 7 typical methods verify the superiority of the proposed method.

关 键 词：武器目标分配 遗传算法 蚁群优化 组合优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]