Cooperative particle swarm optimization using MapReduce
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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.
KeywordsLarge-scale Parallel and distributed computing MapReduce PSO
This work was supported by the National Natural Science Foundation of China (Nos. 61272279, 61272282, 61371201 and 61203303), the National Basic Research Program (973 Program) of China (No. 2013CB329402), the Program for Cheung Kong Scholars and Innovative Research Team in University (No. IRT_15R53) and the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048).
Compliance with ethical standards
Conflict of interest
Yangyang Li, Zhenghan Chen, Yang Wang and Licheng Jiao declare that they no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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