Thammano, Arit

Preferred name

Thammano, Arit

Alternative Name

Thammano, A.

Thummano, Arit

Main Affiliation

School of Information Technology

arit.th@kmitl.ac.th

Now showing 1 - 8 of 8

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A self-adaptive differential evolution algorithm for continuous optimization problems
(2014-09-01)
Jitkongchuen, Duangjai
;
Thammano, Arit
This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.
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Firefly Mating Algorithm for Continuous Optimization Problems
(2017-01-01)
Ritthipakdee, Amarita
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Thammano, Arit
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Premasathian, Nol
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Jitkongchuen, Duangjai
This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.
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Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
(2025-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Jitkongchuen, Duangjai
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Thammano, Arit
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Longpradit, Panchit
This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm Optimization (SCSO) and Invasive Weed Optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and Whale Optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.
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An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems
(2022-01-01)
Jitkongchuen, Duangjai
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Sirikayon, Chaloemphon
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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
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Sukpongthai, Warattha
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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.
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An Improvement of Whale Optimization Algorithm with Rao Algorithm for Optimization Problems
(2023-01-01)
Pravesjit, Sakkayaphop
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Thammano, Arit
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Kantawong, Krittika
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Jitkongchuen, Duangjai
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Longpradit, Panchit
This paper proposes an improvement of whale optimization algorithm for optimization problems. In this study, the Rao algorithm was improved by means of procedures of spiral updating position. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), intersection mutation differential evolution (IMDE) algorithm, and whale optimization algorithm (WOA). The computation results illustrated that the proposed algorithm can produce optimal solutions for three out of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function, and the discontinuous step function.
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Modification of Sand Cat Swarm Optimization for Classification Problems
(2024-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Hunta, Sathien
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Jitkongchuen, Duangjai
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Thammano, Arit
The proposed system represents an enhanced version in search of food of Sand Cat based on Levy distribution and Firework algorithm for the image classification of grape leaf diseases. In the preprocessing step, the proposed system utilizes convolution kernels to transform images into input data within the range of (0,1). Successively, the Levy distribution and Firework algorithm are incorporated into the SCSO model as an exploration search mechanism. The study employed a grape leaf dataset sourced from the Plant Village project (www.plantvillage.org), comprising 4062 labeled images measuring 256 by 256 pixels and categorized into 4 distinct classes: healthy, Black Rot, Black Measles, and Isariopsis leaf spot, which was utilized to evaluate the efficacy of the proposed system. The experimental findings demonstrate that the proposed system outperforms the analyses of VGG16, GLCM with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and SCSO.
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An Improvement on Exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems
(2024-01-01)
Pravesjit, Sakkayaphop
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Kantawong, Krittika
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Kamkhad, Natdanai
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Sabaiporn, Saksit
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Monchanuan, Jantawan
The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (www.plantvillage.org), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).

Thammano, Arit

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