标题:A parallel strategy applied to APSO
作者:Chai, Qing-Wei ;Pan, Jeng-Shyang ;Zheng, Wei-Min ;Chu, Shu-Chuan
通讯作者:Chu, ShuChuan
作者机构:[Chai, Qing-Wei ;Pan, Jeng-Shyang ;Zheng, Wei-Min ;Chu, Shu-Chuan ] Department of Computer Science and Engineering, Shandong University of Science and 更多
会议名称:13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019
会议日期:1 November 2019 through 3 November 2019
来源:Advances in Intelligent Systems and Computing
出版年:2020
卷:1107 AISC
页码:61-68
DOI:10.1007/978-981-15-3308-2_7
关键词:APSO; Parallel APSO; Parallel PSO
摘要:Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms’ performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO. © Springer Nature Singapore Pte Ltd. 2020.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082011461&doi=10.1007%2f978-981-15-3308-2_7&partnerID=40&md5=8dd3080ac7cf4be25177df4adb8fe3dd
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