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Author: search.filters.author.Thammano, AritSubject: search.filters.subject.Swarm intelligence

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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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    Memetic algorithm based on marriage in honey bees optimization for flexible job shop scheduling problem
    (2017-12-01)
    Phu-ang, Ajchara
    ;
    Thammano, Arit
    This paper proposes a new memetic algorithm based on marriage in honey bees optimization (MBO) algorithm for solving the flexible job shop scheduling problem. The proposed algorithm introduces four new features to the standard MBO algorithm, mainly to get the search to move away from the local optimum: (1) the use of a harmony memory to improve the quality of initial population; (2) the introduction of a new crossover operator called triparental crossover to help increase the genetic diversity in the offspring; (3) the addition of adaptive crossover probability (P <inf>c</inf>) and mutation probability (P <inf>m</inf>) to remove the need for users to specify these probabilities; and (4) the incorporation of simulated annealing algorithm embedded with a set of heuristics to enhance the local search capability. The proposed algorithm was evaluated and compared to several state-of-the-art algorithms in the literature. The experimental results on five sets of standard benchmarks show that the proposed algorithm is very effective in solving the flexible job shop scheduling problems.
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    A hybrid artificial bee colony algorithm with local search for flexible job-shop scheduling problem
    (2013-01-01)
    Thammano, Arit
    ;
    Phu-Ang, Ajchara
    This paper presents a hybrid artificial bee colony algorithm for solving the flexible job-shop scheduling problem (FJSP) with the criteria to minimize the maximum completion time (makespan). In solving the FJSP, we have to focus on two sub-problems: determining the sequence of the operations and selecting the best machine for each operation. In the proposed algorithm, first, several dispatching rules and the harmony search algorithm are used in creating the initial solutions. Thereafter, one of the two search techniques is randomly selected with a probability that is proportional to their fitness values. The selected search technique is applied to the initial solution to explore its neighborhood. If a premature convergence to a local optimum happens, the simulated annealing algorithm will be employed to escape from the local optimum. Otherwise, the filter and fan algorithm is utilized. Finally, the crossover operation is presented to enhance the exploitation capability. Experimental results on the benchmark data sets show that the proposed algorithm can effectively solve the FJSP. © 2013 The Authors. Published by Elsevier B.V.
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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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    SMBO: A self-organizing model of marriage in honey-bee optimization
    (2012-04-01)
    Thammano, Arit
    ;
    Poolsamran, Patcharawadee
    This paper proposes a novel swarm intelligence technique, which is an adaptation of Abbass's marriage in honey-bee optimization (MBO), with the aim to achieve better overall performance than the original version of the MBO while also lowering the computation time for finding the optimal solution. The original MBO has been proven to be one of the best swarm intelligence algorithms for solving optimization problems. However, many parameters need to be properly set in order for the MBO to perform at its best. Therefore, long computation time caused by a large number of trial and error iterations involved in trying to find the right combination of parameters is unavoidable. The framework of the proposed algorithm is similar to the original MBO, which is based on the marriage behavior of honey-bees. In order to improve the efficiency of the MBO algorithm, several aspects of the original MBO have been adapted, such as (1) the proposed algorithm is adapted to obtain the ability to automatically search for the proper number of queens, (2) the proposed algorithm divides the problem space into several colonies, each of which has its own queen. In order to keep the number of colonies to a minimum, the proposed algorithm, therefore, encourages the queens to compete with each other for a larger colony and also urges the newly-born brood which is fitter than the queen of the colony to overthrow the queen. (3) the fuzzy c-means algorithm is employed to assign the drones to the proper colonies. The proposed algorithm has been evaluated and compared to the original MBO algorithm. The experimental results on six benchmark problems demonstrate the potential of the proposed algorithm in offering an efficient and effective solution to the problem. © 2011 Elsevier Ltd. All rights reserved.