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Multi-level Competitive Swarm Optimizer for Large Scale Optimization

  • Li Zhang
  • Yu Zhu
  • Si ZhongEmail author
  • Rushi Lan
  • Xiaonan Luo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

In this paper, a new multi-level competitive swarm optimizer (MLCSO) is proposed for large scale optimization. As a variant of particle swarm optimization (PSO), MLCSO first divides the particles of original swarm into two groups randomly and then compares the particles according to their fitness values. The loser with worse fitness value will be put into the first level. The winner with better fitness becomes a new little swarm. New little swarm continues to be divided and compared until the new swarm has only one particle. This process forms a multi-level mechanism. The loser will be updated by the winner. It not only shows a great balance between exploration and exploitation but also enhances the diversity. 20 different kinds of test functions are selected for the experiments. Despite MLCSO algorithm is simple, the experimental results on high-dimension by comparing it with five state-of-the-art algorithms demonstrated its effectiveness.

Keywords

Large scale optimizer Multi-level Competitive Exploration and Exploitation 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Li Zhang
    • 1
  • Yu Zhu
    • 1
  • Si Zhong
    • 1
    Email author
  • Rushi Lan
    • 1
  • Xiaonan Luo
    • 1
  1. 1.Guangxi Key Laboratory of Intelligent Processing of Computer Images and GraphicsGuilin University of Electronic TechnologyGuilinChina

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