A modified multi-swarm optimization with interchange GBEST and particle redistribution

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dc.contributor.authorChengkhuntod, Kanokporn
dc.contributor.authorKruatrachue, Boontee
dc.contributor.authorSiriboon, Kritawan
dc.date.accessioned2026-08-06T10:17:29Z
dc.date.available2026-08-06T10:17:29Z
dc.date.issued2017-10-19
dc.description.abstractThe Particle Swarm Optimization (PSO) is an optimization algorithm using multiples particle to search solution space for an optimize solution. Each particle of PSO moves toward the best solution within its group. For this behavior, PSO often traps in local optimum. Many researchers proposed splitting a swarm into multiple swarms so that they may move to different local optimum. Besides, the mutation operation technique, the natural selection technique and the crossover operation technique are added to normal PSO process. These proposed techniques are called Selective Crossover base on Fitness in Multi-Swarm Optimization (SFMPSO) and Fast Multi-swarm Optimization (FMPSO). However, both techniques used too many evaluation calls dues to crossover and the mutation operation. This paper proposes setting the best position (GBEST) of a trapped swarm to GBEST of the other swarm. Then, the swarm's particle is redistributed in solution space before restart the trapped swarm. This proposed technique is evaluated on a set of twenty-six benchmark test functions. The experimental results show that the results are better than those of PSO, FMPSO and SFMPSO.
dc.identifier.citation2017 International Electrical Engineering Congress Ieecon 2017, 2017
dc.identifier.doi10.1109/IEECON.2017.8075806
dc.identifier.other2-s2.0-85039953187
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7879
dc.source2017 International Electrical Engineering Congress Ieecon 2017
dc.subjectCauchy mutation
dc.subjectMultiple swarm
dc.subjectParticle swarm optimization
dc.subjectSwarm intelligence
dc.titleA modified multi-swarm optimization with interchange GBEST and particle redistribution
dc.typeConference Paper

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