Fast mixed strategy differential evolution using effective mutant vector pool

  • Authors:

  • Hao Liu;Han Huang;Yingjun Wu;Zhenhua Huang

  • Affiliations:

  • School of Software Engineering, South China University of Technology, Guangzhou, P.R. China;School of Software Engineering, South China University of Technology, Guangzhou, P.R. China,Department of Management Sciences, College of Business City University of Hong Kong, Hong Kong;School of Software Engineering, South China University of Technology, Guangzhou, P.R. China;School of Software Engineering, South China University of Technology, Guangzhou, P.R. China

  • Venue:
  • ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
  • Year:
  • 2012

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Abstract

The mutant vector has significant influence on the performance of Differential Evolution (DE). Different mutant vector always generates different result, one outstanding mutant vector for a specify problem perhaps achieve unbearable bad result for another question. There still no one perfect mutant vector can solve all problems excellently. In this situation, mixed strategy method is proposed to improve the performance of DE by combining multi-effective mutant vectors together. This paper proposes a fast mixed strategy DE (FMDE). The new method uses two best mutant vectors selected from the mutant vector pool and applies a fast mixed method to generate better result without increase computing expense. The FMDE is evaluated by 27 benchmarks selected from Congress on Evolutionary Computation (CEC) competition. The experiment result shows FMDE is competitive, stable and comprehensive. abstract environment.