A modified particle swarm optimization with mutation and reposition

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dc.contributor.authorRatanavilisagul, Chiabwoot
dc.contributor.authorKruatrachue, Boontee
dc.date.accessioned2026-08-06T10:10:12Z
dc.date.available2026-08-06T10:10:12Z
dc.date.issued2014-12-01
dc.description.abstractThe common problems of particle swarm optimization (PSO) are those of trapping in local optimum and premature convergence. This research paper aims to develop a solution to both problems by introducing mutation around particles and employing the reposition technique. The concurrent use of the introduced mutation and reposition has proved to solve both problems and enhanced the PSO performance; and thus is employed in this research. The proposed technique is termed MRPSO. MRPSO is tested on sixteen benchmark functions and the multidimensional knapsack problems (MKP). MRPSO yields the more satisfactory search results than the genetic algorithm (GA) and PSOs for the benchmark functions and the MKPs.
dc.identifier.citationInternational Journal of Innovative Computing Information and Control, 10(6), 2127-2142, 2014
dc.identifier.issn13494198
dc.identifier.other2-s2.0-84923381421
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5875
dc.sourceInternational Journal of Innovative Computing Information and Control
dc.subjectBinary particle swarm optimization
dc.subjectGenetic algorithm
dc.subjectMultidimensional knapsack problem
dc.subjectMutation operator
dc.subjectParticle swarm optimization
dc.titleA modified particle swarm optimization with mutation and reposition
dc.typeArticle

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