Hybridisation of particle swarm optimization and fast evolutionary programming

Authors:
Jingsong He;Zhengyu Yang;Xin Yao
Affiliations:
Nature Inspired Computation and Applications Laboratory;Nature Inspired Computation and Applications Laboratory;Nature Inspired Computation and Applications Laboratory
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 4
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Abstract

Particle swarm optimization (PSO) and fast evolutionary programming (FEP) are two widely used population-based optimisation algorithms. The ideas behind these two algorithms are quite different. While PSO is very efficient in local converging to an optimum due to its use of directional information, FEP is better at global exploration and finding a near optimum globally. This paper proposes a novel hybridisation of PSO and FEP, i.e., fast PSO (FPSO), where the strength of PSO and FEP is combined. In particular, the ideas behind Gaussian and Cauchy mutations are incorporated into PSO. The new FPSO has been tested on a number of benchmark functions. The preliminary results have shown that FPSO outperformed both PSO and FEP significantly.