Sirikayon, Chaloemphon
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Sirikayon, Chaloemphon
Main Affiliation
Email
chaloemphon.si@kmitl.ac.th
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Item type:Publication, An Adaptive Whale Optimization Algorithm with Mahalanobis Distance for Optimization Problems(2022-01-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deterministic Initialization of k-means Clustering by Data Distribution Guide(2022-01-01); Clustering by the k-means is the most widely used method because of its ease of use. But the disadvantage of the k-means algorithm is that it relies on a random initialization. Therefore, the results obtained from each clustering are not stable depending on the starting point, affecting the results obtained in other applications. This paper, therefore, presents a method for determining the initialization of the k-means algorithm using the Data Distribution Guide (DDG). And use it as an aid in determining the starting point without random. Make the results of clustering always equal. And from the experimental results, We found that the accuracy obtained from clustering using the initialization from this method was good. Compared to the commonly used initialization designation.