Abstract
A novel PSO algorithm called InformPSO is introduced in this paper. The premature convergence problem is a deficiency of PSOs. First, we analyze the causes of premature convergence for conventional PSO. Second, the principles of information diffusion and clonal selection are incorporated into the proposed PSO algorithm to achieve a better diversity and break away from local optima. Finally, when compared with several other PSO variants, it yields better performance on optimization of unimodal and multimodal benchmark functions.
This paper is supported by the National Natural Science Foundation (10471083) and the Natural Science Fund, Science & Technology Project of Fujian Province (A0310009, A0510023, 2001J005, Z0511008), the 985 Innovation Project on Information Technique of Xiamen University (2004-2007), Academician Fund of Xiamen University, China.
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Lv, Y., Li, S., Chen, S., Jiang, Q., Guo, W. (2006). Particle Swarm Optimization Based on Information Diffusion and Clonal Selection. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_66
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DOI: https://doi.org/10.1007/11903697_66
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