摘要
针对动态武器目标分配算法中计算耗时较长,在动态多目标条件下应用智能算法时普遍存在求解效率低的问题,提出一种多目标简化群优化算法。该算法将简化群优化算法扩展到多目标领域,将全变量更新机制与谐波步长策略相结合,来平衡算法的探索和开发能力,将算法与其他算法用多目标优化测试函数进行多样性和收敛性对比测试分析。实验结果表明,该算法在求解多样性和收敛性上有一定优势,应用于动态武器目标分配问题求解,具有良好的计算效率和求解精度。
Aimed at the long calculation time in the dynamic weapon target assignment algorithm and the low efficiency of the application of intelligent algorithms under dynamic multi-objective conditions,a multi-objective simplified swarm optimization is proposed.The algorithm extended the simplified swarm optimization(SSO)to the multi-objective field,combined the full-variable update mechanism with the harmonic step strategy to balance the exploration and development capabilities of the algorithm.The comparative testing and analysis of diversity and convergence on the proposed algorithm and other algorithms were performed by using multi-objective optimization test function.Experimental results show that the proposed algorithm has certain advantages in solving diversity and convergence.It is applied to dynamic weapon target assignment problem and has good calculation efficiency and accuracy.
作者
邱少明
王雪珂
杜秀丽
冯江惠
王建伟
Qiu Shaoming;Wang Xueke;Du Xiuli;Feng Jianghui;Wang Jianwei(Key Laboratory of Communication and Network,Dalian University,Dalian 116622,Liaoning,China)
出处
《计算机应用与软件》
北大核心
2023年第6期242-249,共8页
Computer Applications and Software
基金
中央军委装备发展部领域基金项目(61400010301)。
关键词
动态武器-目标分配
简化群优化算法
多目标优化
谐波步长策略
Weapon target assignment
Simplified swarm optimization
Multi-objective optimization
Harmonic step strategy
