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Author: search.filters.author.Kruatrachue, BoonteeSubject: search.filters.subject.Particle swarm optimization

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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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    A modified multi-swarm optimization with interchange GBEST and particle redistribution
    (2017-10-19)
    Chengkhuntod, Kanokporn
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    Kruatrachue, Boontee
    ;
    Siriboon, Kritawan
    The 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.
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    A modified particle swarm optimization with mutation and reposition
    (2014-12-01)
    Ratanavilisagul, Chiabwoot
    ;
    Kruatrachue, Boontee
    The 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.