Soft Computing

, Volume 21, Issue 22, pp 6593–6603 | Cite as

Cooperative particle swarm optimization using MapReduce

Foundations

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.

Keywords

Large-scale Parallel and distributed computing MapReduce PSO 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and ComputationXidian UniversityXi’anChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and Technology (NUIST)NanjingChina

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