Abstract
Similar with other swarm algorithms, the PSO algorithm also suffers from premature convergence. Mutation is a widely used strategy in the PSO algorithm to overcome the premature convergence. This paper discusses some induction patterns of mutation (IPM) and typical algorithms, and then presents a new PSO algorithm – the Limited Mutation PSO algorithm. Basing on a special PSO model depicted as “social-only”, the LMPSO adopts a new mutation strategy – limited mutation. When the distance between one particle and the global best location is less than a threshold predefined, some dimensions of the particles will mutate under specific rules. The LMPSO is compared to other five different types of PSO with mutation strategy, and the experiment results show that the new algorithm performances better on a four-function test suite with different dimensions.
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Song, C., Zhao, H., Cai, W., Zhang, H., Zhao, M. (2007). The Limited Mutation Particle Swarm Optimizer. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_29
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DOI: https://doi.org/10.1007/978-3-540-74769-7_29
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