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A grouping particle swarm optimizer

  • Xiaorong Zhao
  • Yuren ZhouEmail author
  • Yi Xiang
Article
  • 8 Downloads

Abstract

Due to the lack of global search capacity, most evolutionary or swarm intelligence based algorithms show their inefficiency when optimizing multi-modal problems. In this paper, we propose a grouping particle swarm optimizer (GPSO) to solve this kind of problem. In the proposed algorithm, the swarm consists of several groups. For every several iterations, an elite group is constructed and used to replace the worst one. The thought of grouping is helpful for improving the diversity of the solutions, and then enhancing the global search ability of the algorithm. In addition, we apply a simple mutation operator to the best solution so as to help it escape from local optima. The GPSO is compared with several variants of particle swarm optimizer (PSO) and some state-of-the-art evolutionary algorithms on CEC15 benchmark functions and three practical engineering problems. As demonstrated by the experimental results, the proposed GPSO outperforms its competitors in most cases.

Keywords

Evolutionary algorithm Grouping PSO Multi-modal 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Engineering Research InstituteGuangzhou College of South China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.School of Data and Computer Science, Collaborative Innovation Center of High Performance ComputingSun Yat-sen UniversityGuangzhouPeople’s Republic of China
  3. 3.School of Software EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China

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