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Soft Computing

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13.10.2016 | Foundations

Cooperative particle swarm optimization using MapReduce

verfasst von: Yang Wang, Yangyang Li, Zhenghan Chen, Yu Xue

Erschienen in: Soft Computing | Ausgabe 22/2017

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Abstract

Cooperative particle swarm optimization (short in CPSO) is an effective evolutionary algorithm for optimization and has attracted a lot of research attention. As real-world optimization problems become complex and large scale, population-based optimization algorithms may take a long time to complete a task. Responding to this trend, CPSO, as a serial evolutionary algorithm, also needs to be updated and accelerated. On the other hand, MapReduce is a programming model for parallel computation and accelerates many tasks successfully. In this paper, we present MapReduce cooperative particle swarm optimization (short in MRCPSO) which implements CPSO-S, a version of CPSO, using MapReduce model. MRCPSO is compared with the original CPSO-S and two algorithms in CEC 2013 special session and competition on real-parameter single-objective optimization. The result on benchmarks shows that MRCPSO outperforms the original CPSO-S significantly on both time and the quality of solution. And in the comparisons with the other two algorithms, MRCPSO has better performance on several problems, while the advantage on time is significant.

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Titel: Cooperative particle swarm optimization using MapReduce
verfasst von: Yang Wang
Yangyang Li
Zhenghan Chen
Yu Xue
Publikationsdatum: 13.10.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 22/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2390-9

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