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Enhance particle's exploration of particle swarm optimization with individual particle mutation
Author(s)
Adsawinnawanawa, Ekkarat
Kruatrachue, Boontee
Siriboon, Kritawan
Date Issued
March 1, 2019
Type
Conference Paper
Abstract
This paper proposed the restart of slow improvement particle with Mutation of its position. This can alleviate the trapped in local optima problem of Particle swarm optimization (PSO). The main characteristic of this method is the gradually restart of particle instead of restart all particle at the same time (rerun). This seems to maintain fast convergence of PSO and avoid overhead of restart the whole swarm. The performance of the proposed method is compared to others algorithms with 26 benchmark test functions. The results confirm the faster convergence with optimum results of the proposed method in most test functions.
Citation
Ieecon 2019 7th International Electrical Engineering Congress Proceedings, 2019