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arXiv · 1308.1484

A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments

Somayeh Nabizadeh·Alireza Rezvanian·Mohammd Reza Meybodi
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Abstract

Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make a sub-swarm, are evolved by the particle swarm optimization and clonal selection algorithm. Experimental results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its popular methods.

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Somayeh Nabizadeh, Alireza Rezvanian, Mohammd Reza Meybodi. 2013-08-07. A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments. https://doi.org/10.1109/iciev.2012.6317524

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