改进MOPSO的联合火力打击目标分配  

Improved MOPSO Joint Fire Strike Target Assignment

作　　者：陈曼 周凤星[1] 张成尧 CHEN Man;ZHOU Feng-xing;ZHANG Cheng-yao(School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081,China;Wuhan Yixin Hengtong Technology Co.,Ltd., Wuhan 430073,China)

机构地区：[1]武汉科技大学信息科学与工程学院,武汉430081 [2]武汉义信恒通科技有限公司,武汉430073

出　　处：《火力与指挥控制》2019年第9期125-129,134,共6页Fire Control & Command Control

基　　金：国家自然科学基金资助项目(61174106)

摘　　要：在舰载联合火力打击的武器-目标分配问题中,针对失败概率最小和使用武器最少原则,设计了一种改进的多目标粒子群优化算法(MOPSO)来进行分配的优化。将自适应变异方法用于更新外部档案集;随机挑选外部档案集内依据拥挤距离由大到小排在前5%的解当作全局最优值;通过利用改进的学习因子和惯性权重来更新粒子。仿真结果表明,设计的算法比带精英机制的非支配排序遗传算法(NSGA-Ⅱ)运行速度更快、求得的Pareto前沿解的精度更高,能有效地解决舰载联合火力打击目标分配问题。In the weapon target assignment of shipboard fire strike,an improved hybrid multi-objective particle swarm optimization algorithm(MOPSO) is designed to minimize the probability of failure and the use of weapons. Add adaptive mutation operator when updating external file set.Randomly selected external files based on the crowded distance in descending order of the top 5 %solution as the global optimal value. Updating particles with improved learning factor and inertia weight. The simulation results show that the designed algorithm has higher convergence precision Pareto solutions and faster average operation than the Second generation non-dominated sorting genetic algorithm(NSGA-Ⅱ), which can effectively solve the problem of weapon target assignment of shipboard fire strike.

关 键 词：多目标粒子群 武器-目标分配 自适应变异 拥挤距离 惯性权重 

分 类 号：TJO1[兵器科学与技术] TJ83