A modified particle swarm optimizer with a novel operator

Authors:
Ran Cheng;Min Yao
Affiliations:
College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Year:
2010

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Abstract

This paper proposes a simple and effective modified particle swarm optimizor with a novel operator. The aim is to prevent premature convergence and improve the quality of solutions. The standard PSO is shown to have no ability to perform a fine grain search to improve the quality of solutions as the number of iterations is increased, although it may find the near optimal solutions much faster than other evolutionary algorithms. The modified PSO algorithm presented in this paper is able to find near optimal solutions as fast as the standard PSO and improve their quality in the later iterations. Compared with the standard PSO, benchmark tests are implemented and the result shows that our modified algorithm successfully prevents premature convergence and provides better solutions.