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

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 4; pp. 2030 - 2038
• Main Authors: Cao, Bin, Zhao, Jianwei, Lv, Zhihan, Liu, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Big Data; Convergence; Cooperative coevolution; Data management; Decision analysis; decomposition; distributed parallelism; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; large-scale optimization; Message passing; message passing interface (MPI); Multiple objective analysis; Optimization; Optimization algorithms; Parallel processing; Sociology; Statistics; variable grouping
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2017.2676000

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.