Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

Article

Abstract

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.

Keywords

global optimization statistical learning differential evolution particle swarm optimization hybridization multimodal functions 

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Copyright information

© Science in China Press and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.School of Automatic ControlBeijing Institute of TechnologyBeijingChina
  2. 2.Key Laboratory of Complex System Intelligent Control and DecisionMinistry of EducationBeijingChina

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