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Author: search.filters.author.Kruatrachue, BoonteeSubject: search.filters.subject.Swarm intelligence

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    Improving Multi-Swarm by Slightly Mutation Particle and GBEST of Stuck Swarm Along with Randomly Selecting GBEST of Other Swarm
    (2018-08-21)
    Chengkhuntod, Kanokporn
    ;
    Kruatrachue, Boontee
    ;
    Siriboon, Kritawan
    This paper proposed another approach in handling trapping in local optimum problem of Particle Swarm Optimization (PSO) using multi-swarm. Since each swarm might trap in different local optimum, the trapped swarm restart with slightly mutation (15% of each particle attributes) along with swaying swarm by randomly use of other swarm GBEST position. In the case of all swarm trapping in the same location, the trap GBEST is also slightly mutate in the same way as particle position. This proposed technique is tested on a set of twenty-four benchmark test functions. The experimental results show that the proposed method is better than other comparing methods.
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    A modified multi-swarm optimization with interchange GBEST and particle redistribution
    (2017-10-19)
    Chengkhuntod, Kanokporn
    ;
    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.