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Author: search.filters.author.Kruatrachue, BoonteeSubject: search.filters.subject.Mutation

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    A perturbed particle swarm optimization using harmony search and mutation
    (2020-07-01)
    Phuchan, Wikrom
    ;
    Kruatrachue, Boontee
    This paper applies the harmony search (HS) and mutation to lessens the stagnant of PSO. When a particle stops improving, it is mutated or replace by a position value created from harmony memory, which memorized improve locations of particles (Pbest) in search space. Another HS generates the best position among all particles (Gbest) to replace stagnant Gbest to sway the swarm from the trapping location. This HS has another harmony memory that contains improving Gbest. The results of the proposed algorithm are compared with related modified-PSO using 27 benchmark functions. The proposed algorithm locates optimum in more benchmark functions with faster execution.
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    Mutation Variations in Improving Local Optima Problem of PSO
    (2020-01-01)
    Adsawinnawanawa, Ekkarat
    ;
    Kruatrachue, Boontee
    This paper experiment on various concepts in performing mutation to lessen trap in a local optima problem of Particle swarm optimization (PSO). The first concept is when to perform mutation. The earlier mutation favors exploration more than exploitation and usually leads to slow convergence, while the late mutation tends to have opposite characteristics. The second concept is the reset of a known best position (GBEST) when trapping in local optima. The reset reduces the chance of trapping in the same local optima but may lead to slower convergence. On the other hand, mutations without reset best position exploit previous knowledge and converge faster if the GBEST closes to optima. The performances of each concept are compared using 27 benchmark test functions. The results are mixing, but the early mutation without reset GBEST perform better in many of test function.
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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.