The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems

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dc.contributor.authorNootyaskool, Supakit
dc.date.accessioned2026-08-06T10:05:00Z
dc.date.available2026-08-06T10:05:00Z
dc.date.issued2012-10-26
dc.description.abstractGenetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly. © 2012 IEEE.
dc.identifier.citationProceedings of the 2012 4th International Conference on Knowledge and Smart Technology Kst 2012, 57-61, 2012
dc.identifier.doi10.1109/KST.2012.6287739
dc.identifier.other2-s2.0-84867736571
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4407
dc.sourceProceedings of the 2012 4th International Conference on Knowledge and Smart Technology Kst 2012
dc.subjectgenetic algorithm
dc.subjecthybrid technique
dc.subjectnumerical optimization
dc.subjectparticle swarm optimization
dc.titleThe hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems
dc.typeConference Paper

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