Thammano, Arit

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Thammano, Arit
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Thammano, A.
Thummano, Arit
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arit.th@kmitl.ac.th

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    An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems
    (2022-01-01)
    Jitkongchuen, Duangjai
    ;
    Sirikayon, Chaloemphon
    ;
    Thummano, Arit
    This paper suggests using Mahalanobis distance to regenerate a new whale position to increase the performance of the whale optimization algorithm. Learning from previous evolutionary searches allows the probability parameters to be self-adapted. The suggested approach was compared to the classical whale optimization algorithm (WOA), particle swarm optimization (PSO), and differential evolution algorithm (DE) on 11 well-known benchmark functions. The results of the experiments showed that the proposed algorithm was effective in solving optimization problems.
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    Weighted distance grey Wolf optimization with immigration operation for global optimization problems
    (2017-08-29)
    Jitkongchuen, Duangjai
    ;
    Sukpongthai, Warattha
    ;
    Thammano, Arit
    The proposed algorithm presents a solution to improve the grey wolf optimizer performance using weighted distance and immigration operation. The weight distance is used for the omega wolves movement is defined from fitness value of each leader (alpha, beta and delta). The traditional grey wolf algorithm has only one pack and has opportunity to trap in local optimum so the wolves in our proposed algorithm have more pack and have migrated between them. When the amount of pack has more than to predefine some pack will be eliminated. The experimental results are evaluated by a comparative with the traditional grey wolf optimizer (GWO) algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithm on 9 well-known benchmark functions. The experimental results showed that the proposed algorithm is capable of efficiently to solving complex optimization problems.