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An adaptive parallel particle swarm optimization for numerical optimization problems

  • Xinsheng Lai
  • Yuren Zhou
Original Article
  • 82 Downloads

Abstract

The parallelization of particle swarm optimization (PSO) is an efficient way to improve the performance of PSO. The multiple population parallelization is one way to parallelize PSO, in which three parameters need to be manually set in advance. They are migration interval, migration rate, and migration direction, which decide when, how many and from which subpopulation to which subpopulation particles will be migrated, respectively. However, there are two shortcomings concerning manually setting these three parameters in advance. One is that good particles cannot be migrated in time since particles can only be migrated every a given interval and in a given direction in parallel PSO. The other is that a large number of unnecessary migrations will take place since a given rate of particles in each subpopulation will be migrated every a given interval in a given direction. Both may be bad for parallel PSO to find high-quality solutions as quickly as possible, and this will result in a huge communication cost. Inspired by the phenomenon of osmosis, this paper presents a multiple population parallel version of PSO based on osmosis. It can adaptively decide when, how many, and from which subpopulation to which subpopulation particles will be migrated. Its usefulness, especially for high-dimensional functions, is demonstrated by numerical experiments.

Keywords

PSO Parallel Multiple population Osmosis Migration Adaptive 

Notes

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (Grant numbers 61562071, 61773410, 61165003, 61472143), the Scientific Research Special Plan of Guangzhou Science and Technology Programme (Grant no. 201607010045), and the Natural Science Foundation of Jiangxi Province (Grant no. 20151BAB207020).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Mathematics and Computer ScienceShangrao Normal UniversityShangraoChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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