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Author: search.filters.author.Siriboon, KritawanSubject: search.filters.subject.Particle Swarm Optimization

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    Enhance particle's exploration of particle swarm optimization with individual particle mutation
    (2019-03-01)
    Adsawinnawanawa, Ekkarat
    ;
    Kruatrachue, Boontee
    ;
    Siriboon, Kritawan
    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.
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    Combine multi particle swarm in supporting trapping in local optima
    (2018-08-13)
    Poempool, Lukkana
    ;
    Kruatrachue, Boontee
    ;
    Siriboon, Kritawan
    This paper proposed using multi swarm to lessen trapping in local optima problem of Particle Swarm Optimization (PSO). The use of multiple swarms can increase wider global search at the cost of decrease narrower local search. Hence, the use of multiple swarms alone can't solve the trapping problem. This paper extends the use of multi swarm by merging all the swarm into single group to enhanced local search when needed. This can increase the local search ability of multi swarm and increase the chance of the trapped swarm to move to other nearby local optima. The proposed method is compared to similarly comparable modified PSO and other similarly multi particle swarm without merging using 26 benchmark functions.