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Author: search.filters.author.Thammano, AritSubject: search.filters.subject.Genetic Algorithm

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    Enhancing Genetic Algorithm Performance with Hybrid Strategy for Solving Optimization Problems
    (2023-01-01)
    Farda, Irfan
    ;
    Thammano, Arit
    This study presents a novel algorithm called hybrid-GA, which combines genetic algorithm (GA) with the Harris Hawks Optimization (HHO) algorithm to address the challenge of enhancing GA performance in solving optimization problems. While GA is known for its strong exploration capabilities, it often faces challenges in exploitation, limiting its ability to find global optimal solutions. The hybrid-GA algorithm aims to surpass existing methods by achieving a better balance between exploration and exploitation, resulting in improved solution quality, faster convergence, and enhanced exploration-exploitation ability. The algorithm effectiveness is demonstrated through experiments on six benchmark functions from CEC2017, where the hybrid-GA outperforms compared algorithms on five of six functions, showcasing its potential for enhancing GA performance in optimization problem-solving. These findings contribute to advancing the field by providing a promising solution to address the exploration-exploitation challenge in GA-based optimization.
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    A new selection operator to improve the performance of genetic algorithm for optimization problems
    (2013-11-25)
    Ritthipakdee, Amarita
    ;
    Thammano, Arit
    ;
    Premasathian, Nol
    ;
    Uyyanonvara, Bunyarit
    Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions. © 2013 IEEE.
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    Modified genetic algorithm for flexible job-shop scheduling problems
    (2012-01-01)
    Teekeng, Wannaporn
    ;
    Thammano, Arit
    This paper proposes a modified version of the genetic algorithm for flexible job-shop scheduling problems (FJSP). The genetic algorithm (GA), a class of stochastic search algorithms, is very effective at finding optimal solutions to a wide variety of problems. The proposed modified GA consists of 1) an effective selection method called "fuzzy roulette wheel selection," 2) a new crossover operator that uses a hierarchical clustering concept to cluster the population in each generation, and 3) a new mutation operator that helps in maintaining population diversity and overcoming premature convergence. The objective of this research is to find a schedule that minimizes the makespan of the FJSP. The experimental results on 10 well-known benchmark instances show that the proposed algorithm is quite efficient in solving flexible job-shop scheduling problems. © 2012 Published by Elsevier B.V.
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    A new fuzzy-neural system for time series forecasting
    (2005-11-30)
    Thammano, Arit
    ;
    Palahan, Sirinda
    This paper proposes a new time series forecasting system, whose learning algorithm is a hybrid of the fuzzy c-means algorithm, the genetic algorithm, and the backpropagation algorithm. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the rule layer, the hidden layer, and the output layer. The fuzzy cmeans algorithm is used to determine the center and width of the fuzzy membership functions. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is used to optimize the fuzzy rule-base. This proposed system is tested with five time series data. The results obtained are very encouraging.