A Parallel Implementation of Multiobjective Particle Swarm Optimization Algorithm Based on Decomposition
- Authors
- Li, Jin-Zhou; Chen, Wei-Neng; Zhang, Jun; Zhan, Zhi-hui
- Issue Date
- Dec-2015
- Publisher
- IEEE
- Citation
- 2015 IEEE Symposium Series on Computational Intelligence, pp 1310 - 1317
- Pages
- 8
- Indexed
- SCI
SCOPUS
- Journal Title
- 2015 IEEE Symposium Series on Computational Intelligence
- Start Page
- 1310
- End Page
- 1317
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/116381
- DOI
- 10.1109/SSCI.2015.187
- Abstract
- Multiobjective particle swarm optimization based on decomposition (MOPSO/D) is an effective algorithm for multiobjective optimization problems (MOPs). This paper proposes a parallel version of MOPSO/D algorithm using both message passing interface (MPI) and OpenMP, which is abbreviated as MO-MOPSO/D. It adopts an island model and divides the whole population into several subspecies. Based on the hybrid of distributed and shared-memory programming models, the proposed algorithm can fully use the processing power of today's multicore processors and even a cluster. The experimental results show that MO-MOPSO/D can achieve speedups of 2x on a personal computer equipped with a dual-core four-thread CPU. In terms of the quality of solutions, it can perform similarly to the serial MOPSO/D but greatly outperform NSGA-II. An additional experiment is done on a cluster, and the results show the speedup is not obvious for small-scale MOPs and it is more suitable for solving highly complex problems.
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Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
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