A Distributed Parallel Cooperative Coevolutionary Multiobjective Evolutionary Algorithm for Large-Scale Optimization

被引:112
作者
Cao, Bin [1 ,2 ,3 ]
Zhao, Jianwei [1 ,2 ,3 ]
Lv, Zhihan [4 ]
Liu, Xin [5 ]
机构
[1] Hebei Univ Technol, Sch Comp Sci & Engn, Tianjin 300401, Peoples R China
[2] Sun Yat Sen Univ, Key Lab Machine Intelligence & Adv Comp, Guangzhou 510275, Guangdong, Peoples R China
[3] Hebei Prov Key Lab Big Data Calculat, Tianjin 300401, Peoples R China
[4] UCL, Dept Comp Sci, London WC1E 6EA, England
[5] Hebei Univ Technol, Tianjin 300401, Peoples R China
基金
中国国家自然科学基金;
关键词
Cooperative coevolution; decomposition; distributed parallelism; large-scale optimization; message passing interface (MPI); variable grouping;
D O I
10.1109/TII.2017.2676000
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
A considerable amount of research has been devoted to multiobjective optimization problems. However, few studies have aimed at multiobjective large-scale optimization problems (MOLSOPs). To address MOLSOPs, which may involve big data, this paper proposes a message passing interface MPI -based distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA). DPCCMOEA tackles MOLSOPs based on decomposition. First, based on a modified variable analysis method, we separate decision variables into several groups, each of which is optimized by a subpopulation (species). Then, the individuals in each subpopulation are further separated to several sets. DPCCMOEA is implemented with MPI distributed parallelism and a two-layer parallel structure is constructed. We examine the proposed algorithm using the multiobjective test suites Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group. In comparison with cooperative coevolutionary generalized differential evolution 3 and multiobjective evolutionary algorithm based on decision variable analyses, which are state-of-the-art cooperative coevolutionary multiobjective evolutionary algorithms, experimental results show that the novel algorithm has better performance in both optimization results and time consumption.
引用
收藏
页码:2030 / 2038
页数:9
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