LS-SVM based on chaotic particle swarm optimization with simulated annealing

Authors:
Ai-ling Chen;Zhi-ming Wu;Gen-ke Yang
Affiliations:
Department of Automation, Shanghai Jiao Tong University, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Venue:
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Year:
2006

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Abstract

The generalization performance of LS-SVM depends on a good setting of its parameters. Chaotic particle swarm optimization (CPSO) with simulated annealing algorithm (SACPSO) is proposed to choose the parameters of LS-SVM automatically. CPSO adopts chaotic mapping with certainty, ergodicity and the stochastic property, possessing high search efficiency. SA algorithm employs certain probability to improve the ability of PSO to escape from a local optimum. The results show that the proposed approach has a better generalization performance and is more effective than LS-SVM based on particle swarm optimization.