摘要
海上搜寻任务通常由多个设施协作完成。针对海上协作搜寻计划制定问题,提出一种记忆库粒子群算法。该算法利用组合优化策略和连续优化策略,首先为单个设施生成相应的备选解并构建记忆库,通过从记忆库中学习、随机生成两种方式生成新的备选解;然后采用网格法更新记忆库,每个网格中最多有一个备选解保存在记忆库中,保证记忆库中备选解的多样性,基于此对解空间进行有效的全局搜索;最后通过从记忆库中随机选择多个备选解组合生成初始协作搜寻方案,利用粒子群策略围绕质量较好的备选解进行有效的局部搜索。实验结果表明,在效率方面,所提算法运行时间较短,在获取最小方差的同时可提高1%~5%的任务成功率,可有效应用于海上协作搜寻计划制定。
Maritime search tasks are usually completed by multiple facilities.In view of the maritime cooperative search planning problem,a Memory Bank Particle Swarm Optimization(MBPSO)algorithm was proposed.Combinatorial optimization strategy and continuous optimization strategy were employed.The candidate solutions and memory bank for every single facility were constructed at first.New candidate solutions were generated based on memory consideration and random selection.Then the memory bank was updated based on a method of lattice,which means that for each lattice,there was only one candidate solution to be stored in the memory bank at most.Based on that,the diversity of the solutions in the memory bank could be ensured and effective global search was performed.At last,initial cooperative search plans were generated by combing candidate solutions in the memory bank randomly.Based on the strategy of Particle Swarm Optimization(PSO),effective local search was performed by searching around the solutions with high quality.Experimental results show that,in terms of efficiency,the time consumed by the proposed algorithm is short;the lowest variance is acquired and the success probability can be increased by 1%to 5%.The proposed algorithm can be applied to make maritime cooperative search plans effectively.
作者
吕进锋
赵怀慈
LYU Jinfeng;ZHAO Huaici(Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang Liaoning 110016,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Opto-Electronic Information Processing(Chinese Academy of Sciences),Shenyang Liaoning 110016,China;Liaoning Key Laboratory of Image Understanding and Computer Vision(Shenyang Institute of Automation, Chinese Academy of Sciences),Shenyang Liaoning 110016,China)
出处
《计算机应用》
CSCD
北大核心
2018年第9期2477-2482,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61673371)
国家重点研发计划项目(2017YFE0101300)
"十三五"装备预研领域基金资助项目(61400010102)~~
关键词
海上搜寻
协作
记忆库
粒子群
全局搜索
局部搜索
maritime search
cooperation
memory bank
particle swarm
global search
local search
