JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (6): 23-30.doi: 10.6040/j.issn.1672-3961.1.2016.161

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Distributed particle swarm optimization algorithm based on mapreduce

FAN Debin, DENG Changshou*, YUAN Sihao, TAN Xujie, DONG Xiaogang   

  1. School of Information and Technology, Jiujiang University, Jiujiang 332005, Jiangxi, China
  • Received:2015-12-31 Online:2016-12-20 Published:2015-12-31

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Abstract: A distributed particle swarm optimization algorithm based on MapReduce was proposed through the analysis of the traditional single population particle swarm optimization algorithm, which was used to solve the algorithm problems of decreased efficiency and accuracy in large scale optimization. In the evolutionary process of particle swarm algorithm, the particles velocity and position was updated by using the method of inertia weight, its value was decreased linearly, the accuracy of convergence was improved by adopting the strategies of multi population evolution. The process of the algorithm was parallel through the MapReduce model, which could improve the efficiency of the algorithm effectively. Several popular algorithms were selected and tested on 13 benchmark functions of 500 dimensions and 1 000 dimensions. The results showed that the algorithm has good optimization performance.

Key words: particle swarm optimization, large scale optimization, MapReduce model, distributed, parallel

CLC Number: 

  • TP391
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