Improvement Technique for Group Search Optimization Using Experimental Design Method

This study proposes the use of an experimental design approach in GSO, and a systematic approach to deal with the hyperparameter settings of GSOs and to provide stable algorithmic performance of GSOs through the experimental design approach. To address these two issues, this study explores the combination of hyperparameters that can improve the performance of GSOs using a uniform design. In addition, the Taguchi method and optimal operations were used to derive an excellent combination of parameters that would provide the best value and robustness of the function to provide a stable performance of GSO. The validity of the performance of the proposed method was tested using ten benchmark functions, including three unimodal, three multimodal, and four restricted multimodal functions. The results were compared with the t-distribution test in addition to the mean and standard deviation to analyze their validity. The results of the t-distribution test showed that the p-values obtained for both UD-GSO and R-GSO were less than 0.05, indicating significant differences compared with GSO for both unimodal and multimodal functions. Two restricted multimodal functions are not significantly different, while the other two are below 0.05, indicating significant differences. This shows that the performance obtained using UD-GSO and R-GSO is more effective than the original GSO. UD-GSO and R-GSO provide better and more robust results than GSO. The main contributions of this paper are as follows: (i) This study proposes a uniform design approach to overcome the difficulties of setting hyperparameters in GSO. (ii) This study proposes a Taguchi method and optimal operation to provide a robust calculation for GSO. (iii) The method applied in this study provides systematic parameter design to solve GSO parameter setting and robust result obtaining.