Affiliation:
1. ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR YAZILIMI ANABİLİM DALI
2. ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ
3. DÜZCE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ
Abstract
In this study, a novel hybridization approach, which is called CMASFS and is based on the covariance matrix adaptation evolution strategy (CMA-ES) and the stochastic fractal search (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.
Publisher
Duzce Universitesi Bilim ve Teknoloji Dergisi
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