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Author: search.filters.author.Nootyaskool, SupakitSubject: search.filters.subject.genetic algorithm

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    Comparison solving discrete space on flower pollination algorithm, PSO and GA
    (2016-03-23)
    Rathasamuth, Wanthanee
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    Nootyaskool, Supakit
    In order to find an optimal solution of a research problem, the problem parameters are encoded in discrete space (e.g. bits or integers). Genetic algorithm (GA) performs the discrete space by binary chromosome. Particle swarm optimization (PSO) uses probability and sigmoid function to convert the next bird's velocity into binary bit. Flower pollination algorithm (FPA) is quite new and also has a few number of the paper implements to discrete space. This research uses a cut-off parameter applied on FPA. The main objective is to examine the performance of the algorithms on discrete space and calculate velocity of PSO as well as Levy fight movement of FPA when using discrete parameter. The experiment used eight-numerical functions to test conversion from a real value to a bit value and measure the effectiveness of each algorithm. Experiment result showed that FPA could perform better than PSO and GA.
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    The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems
    (2012-10-26)
    Nootyaskool, Supakit
    Genetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly. © 2012 IEEE.