Parameter optimization of Artificial Bee Colony Algorithm with Response Surface Methodology
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Abstract
The Artificial Bee Colony Algorithm is one of the metaheuristic with excellent performance and simple implementation. But it is well known that a metaheuristic approach is required to optimize the algorithm's parameters. Therefore, this research aims to use the response surface methodology for analyze the optimal level parameters of the artificial bee colony algorithm for continuous variable benchmark problems. To carry out this research, a 3K factorial experimental design was used, with the design parameters consisting of iteration, number of population, and limit. After that, the experimental table was used to collect data, and the data obtained from the experiments was analyzed by the response surface methodology. From the results of the data analysis, it was found that the main effects resulting from the level change of the three parameters, the interaction effect of the bee population and the limit, and the quadratic model were statistically significant at 0.05 (P-value <0.05). The final step was to run the regression model for finding the parameters that resulted in the best answer. The results of the regression model analysis revealed that the optimum parameters of the Artificial Bee Colony Algorithm to solve continuous variable mathematics functions should be set at iteration, bee population, and limits of 900, 40 and 90, respectively.
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ลิขสิทธ์ ของมหาวิทยาลัยเทคโนโลยีราชมงคลพระนครReferences
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