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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    Item type:Publication,
    Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
    (2025-01-01)
    Pravesjit, Sakkayaphop
    ;
    Kantawong, Krittika
    ;
    Jitkongchuen, Duangjai
    ;
    Thammano, Arit
    ;
    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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    Modification of Sand Cat Swarm Optimization for Classification Problems
    (2024-01-01)
    Pravesjit, Sakkayaphop
    ;
    Kantawong, Krittika
    ;
    Hunta, Sathien
    ;
    Jitkongchuen, Duangjai
    ;
    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.