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基于记忆库粒子群算法的海上协作搜寻计划制定
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
Department光电信息技术研究室
Source Publication计算机应用
ISSN1001-9081
2018
Volume38Issue:9Pages:2477-2482
Indexed ByCSCD
CSCD IDCSCD:6323890
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
Abstract

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。

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.

Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21882
Collection光电信息技术研究室
Corresponding Author赵怀慈
Affiliation1.中国科学院沈阳自动化研究所;
2.中国科学院大学;
3.中国科学院光电信息处理重点实验室;
4.辽宁省图像理解与视觉计算重点实验室
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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