Cheng, R.¹, Sun, C.², and Jin, Y¹

1 - Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

2 - Complex System and Computational, Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024 China

2014 IEEE Congress on Evolutionary Computation (CEC)

Most evolutionary algorithms, including particle swarm optimization (PSO) algorithms, involve at least one population (swarm) to realize information exchange or information sharing among different individuals. To enhance the algorithms' global search ability, several multi-swarm PSO algorithms have been proposed. In this paper, a novel multi-swarm evolutionary framework based on a feedback mechanism is introduced. The framework consists of a search operator similar to those in PSO and a mutation strategy, on the top of the feedback mechanism. The framework is compared with a multi-swarm PSO and the canonical PSO on a few widely used benchmarks to demonstrate its performance.

Check this publication at the journal site.