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Author: search.filters.author.Kiatwuthiamorn, JirapornHas files: No

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    Swarm optimization algorithm based on the ant colony life cycle
    (2019-01-01)
    Kiatwuthiamorn, Jiraporn
    ;
    Thammano, Arit
    Optimization is very important to the success of any business. One technique for solving optimization is swarm intelligence; it has been successfully applied to solve a wide range of optimization problems. We devised a new swarm intelligence optimization algorithm based on the cooperative behavior of three different kinds of ants in a colony. Our algorithm consists of both exploration and exploitation processes to achieve better search performance. A new local search, inspired by the foraging of desert ants, was introduced to help the search move away from the local optima. Performance was evaluated on 23 standard benchmark functions of varying complexity. Our algorithm was able to find the global optima in more than 80 percent of the test functions, whereas the second-place algorithm only found around 10 percent of the functions tested.
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    A new algorithm for flexible job-shop scheduling problem based on particle swarm optimization
    (2016-03-01)
    Teekeng, Wannaporn
    ;
    Thammano, Arit
    ;
    Unkaw, Pornkid
    ;
    Kiatwuthiamorn, Jiraporn
    This paper proposes a new algorithm, named EPSO, for solving flexible job-shop scheduling problem (FJSP) based on particle swarm optimization (PSO). EPSO includes two sets of features for expanding the solution space of FJSP and avoiding premature convergence to local optimum. These two sets are as follows: (I) particle life cycle that consists of four features: (1) courting call—increasing the number of more effective offspring (new solutions), (2) egg-laying stimulation—increasing the number of offspring from the better parents (current solutions), (3) biparental reproduction—increasing the diversity of the next generation (iteration) of solutions, and (4) population turnover—succeeding the population (the current set of all solutions) in the previous generation by a population in a new generation that is as able but more diverse than the previous one; and (II) discrete position update mechanism—moving particles (solutions) towards the flight leader (the best solution), namely, interchanging some integers in every solution with those in both the best solution and itself, using similar swarming strategy as the update procedure of the continuous PSO. The basic objective function used was to minimize makespan which is the most important objective, hence, providing the simplest way to measure the effectiveness of the generated solutions. Benchmarking EPSO with 20 well-known benchmark instances against two widely-reported optimization methods demonstrated that it performed either equally well or better than the other two.
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    A novel optimization algorithm based on the natural behavior of the ant colonies
    (2013-01-01)
    Kiatwuthiamorn, Jiraporn
    ;
    Thammano, Arit
    Optimization problem is one of the most challenging problems that has received considerable attention over the last decade. Many metaheuristic methods have been proposed and successfully applied to find the optimal solution. Each technique has its good and bad points. A new optimization technique based on the natural behavior of the ant colonies is proposed in this paper. In this proposed algorithm, the foraging behavior of worker ants is employed for locally searching for better solution while the marriage, breeding, and feeding behaviors are used in reproduction of the new generation. The proposed algorithm has been evaluated on several benchmark problems. The experimental results demonstrate the effectiveness of the proposed algorithm. © 2013 The Authors. Published by Elsevier B.V.
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    Solving classification problems using supervised self-organizing map
    (2007-12-01)
    Thammano, Arit
    ;
    Kiatwuthiamorn, Jiraporn
    This paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self-organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging. ©2007 IEEE.