北京信息科技大学学报（自然科学版）2023，Vol.38Issue(6)：26-32,7.DOI:10.16508/j.cnki.11-5866/n.2023.06.004
基于DAPF与EACO算法的无人机博弈策略
UAV gaming strategy based on DAPF and EACO algorithms
摘要
Abstract
To deal with the dynamic confrontation game problem between unmanned aerial vehicles(UAVs)on both sides,a solution method combining dynamic artificial potential field(DAPF)method and elite ant colony optimization(EACO)algorithm was proposed.Firstly,the dynamic artificial potential field method was adopted to construct a dynamic confrontation game model for the UAVs,with both the enemy and our UAVs as the central players.Secondly,an elite ant colony algorithm was proposed to calculate the Nash equilibrium strategy of the game between the two sides.The algorithm incorporated opposite-learning and division of elite ants to accelerate the convergence speed,and introduced the mutation operation in the genetic algorithm to avoid the problem of local optimal value.Finally,the feasibility and effectiveness of the proposed method were verified by simulation.关键词
无人机/动态人工势场法/对抗博弈/精英蚁群算法Key words
unmanned aerial vehicle(UAV)/dynamic artificial potential field method/confrontation game/elite ant colony algorithm分类
信息技术与安全科学引用本文复制引用
严航,付兴建..基于DAPF与EACO算法的无人机博弈策略[J].北京信息科技大学学报（自然科学版）,2023,38(6):26-32,7.基金项目
国家自然科学基金项目(61973041) （61973041）
