Predicted-velocity particle swarm optimization using game-theoretic approach

  • Authors:

  • Zhihua Cui;Xingjuan Cai;Jianchao Zeng;Guoji Sun

  • Affiliations:

  • ,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, P.R. China;Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, P.R. China;State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, P.R. China

  • Venue:
  • ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
  • Year:
  • 2006

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Abstract

In standard particle swarm optimization, velocity information only provides a moving direction of each particle of the swarm, though it also can be considered as one point if there is no limitation restriction. Predicted-velocity particle swarm optimization is a new modified version using velocity and position to search the domain space equality. In some cases, velocity information may be effectively, but fails in others. This paper presents a game-theoretic approach for designing particle swarm optimization with a mixed strategy. The approach is applied to design a mixed strategy using velocity and position vectors. The experimental results show the mixed strategy can obtain the better performance than the best of pure strategy.