A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES

Authors : Serdar PAÇACI, Okan BİNGÖL, Uğur GÜVENÇ

Pages : 868-907

Doi:10.29130/dubited.1110725

View : 16 | Download : 11

Publication Date : 2023-04-30

Article Type : Research Paper

Abstract :In this study, a novel hybridization approach, which is called CMASFS and is based on the covariance matrix adaptation evolution strategy insert ignore into journalissuearticles values(CMA-ES); and the stochastic fractal search insert ignore into journalissuearticles values(SFS); algorithms. To make the proposed algorithm dynamic, Gaussian walk equations involved in the diffusion process of SFS have been updated and the algorithm decide to use which the Gaussian walk equations. The effectiveness of the proposed algorithm is tested using CEC2017 benchmark functions having unimodal, multimodal, hybrid, and composition functions in 10, 30, 50, and 100 dimensions. The performance of the CMASFS algorithm is compared with 17 metaheuristic algorithms given in the literature over the CEC2017 benchmark functions. According to the results, it is seen that CMASFS is generally obtained better mean error values. Moreover, to show the superiority of the proposed algorithm, Friedman analysis and the Wilcoxon rank-sum test are applied to the test results of the algorithms. The results of the Wilcoxon signed-rank test show that the improvement with the CMASFS algorithm is statistically significant on the majority of the CEC2017. The results of Friedman test verify that the CMASFS is obtained the best rank compared to both the original SFS and other compared algorithms.
Keywords : Optimizasyon algoritması, Meta sezgisel, Kovaryans matrisi adaptasyon evrim stratejisi, Stokastik fraktal arama, CEC 2017 benchmark problemleri