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Alternative TitleMaritime cooperative search planning based on memory bank particle swarm optimization
吕进锋1,2,3,4; 赵怀慈1,3,4
Source Publication计算机应用
Indexed ByCSCD
Contribution Rank1
Funding Organization国家自然科学基金(61673371) ; 国家重点研发计划(2017YFE0101300) ; “十三五”装备预研领域基金(61400010102)
Keyword海上搜寻 协作 记忆库 粒子群 全局搜索 局部搜索


Other Abstract

Maritime search tasks are usually completed by multi facilities. In view of the maritime cooperative search planning problem, a Memory Bank Particle Swarm Optimization (MBPSO) was proposed. Combinatorial optimization strategy and continuous optimization strategy were employed. The candidate solutions and memory bank for one 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%~5%. The proposed algorithm can be applied to make maritime cooperative search plans effectively.

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Document Type期刊论文
Corresponding Author赵怀慈
Recommended Citation
GB/T 7714
吕进锋,赵怀慈. 基于记忆库粒子群算法的海上协作搜寻计划制定[J]. 计算机应用,2018,38(9):2477-2482.
APA 吕进锋,&赵怀慈.(2018).基于记忆库粒子群算法的海上协作搜寻计划制定.计算机应用,38(9),2477-2482.
MLA 吕进锋,et al."基于记忆库粒子群算法的海上协作搜寻计划制定".计算机应用 38.9(2018):2477-2482.
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