A Particle Swarm Optimization Algorithm Based on Genetic Selection Strategy

  • Authors:

  • Qin Tang;Jianyou Zeng;Hui Li;Changhe Li;Yong Liu

  • Affiliations:

  • Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China 430074 and School of Mathematics and Physics, China University of Geosciences, Wuhan, Chi ...;School of Art and Communication, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074;Department of Computer Science, University of Leicester, Leicester, UK LE1 7RH;Faculty of Mechanical and Electronic Information, China University of Geosciences, Wuhan, China 430074

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

The standard particle swarm optimization algorithm (simply called PSO) has many advantages such as rapid convergence. However, a major disadvantage confronting the PSO algorithm is that they often converge to some local optimization. In order to avoid the occurrence of premature convergence and local optimization of the PSO algorithm, a particle swarm optimization algorithm based on genetic selection stra-tegy, simply called GSS-PSO, is singled out in this paper. GSS-PSO not only retains the rapid convergence charactering of the standard PSO algorithms, but also scales up their global search ability. At last, we experimentally tested the efficiency of our new GSS-PSO algorithm using eight classical functions. The experimental results show that our new GSS-PSO algorithm is generally better than the PSO algorithm.