A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Düzce University
2023-04-01
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| Series: | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/2404278 |
| _version_ | 1797300809045639168 |
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| author | Uğur Güvenç Okan Bingöl Serdar Paçacı |
| author_facet | Uğur Güvenç Okan Bingöl Serdar Paçacı |
| author_sort | Uğur Güvenç |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T23:12:27Z |
| format | Article |
| id | doaj.art-da78f98b12e746ef8204874bc4d2458e |
| institution | Directory Open Access Journal |
| issn | 2148-2446 |
| language | English |
| last_indexed | 2024-03-07T23:12:27Z |
| publishDate | 2023-04-01 |
| publisher | Düzce University |
| record_format | Article |
| series | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| spelling | doaj.art-da78f98b12e746ef8204874bc4d2458e2024-02-21T14:07:33ZengDüzce UniversityDüzce Üniversitesi Bilim ve Teknoloji Dergisi2148-24462023-04-0111286890710.29130/dubited.111072597A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ESUğur Güvenç0Okan Bingöl1Serdar Paçacı2DÜZCE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR YAZILIMI ANABİLİM DALIIn 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.https://dergipark.org.tr/tr/download/article-file/2404278optimization algorithmmeta-heuristiccovariance matrix adaptation evolution strategystochastic fractal searchcec 2017 benchmark problemsoptimizasyon algoritmasımeta-sezgiselkovaryans matrisi adaptasyon evrim stratejisistokastik fraktal aramacec 2017 benchmark problemleri |
| spellingShingle | Uğur Güvenç Okan Bingöl Serdar Paçacı A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES Düzce Üniversitesi Bilim ve Teknoloji Dergisi optimization algorithm meta-heuristic covariance matrix adaptation evolution strategy stochastic fractal search cec 2017 benchmark problems optimizasyon algoritması meta-sezgisel kovaryans matrisi adaptasyon evrim stratejisi stokastik fraktal arama cec 2017 benchmark problemleri |
| title | A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES |
| title_full | A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES |
| title_fullStr | A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES |
| title_full_unstemmed | A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES |
| title_short | A novel hybrid algorithm based on Stochastic Fractal Search Algorithm and CMA-ES |
| title_sort | novel hybrid algorithm based on stochastic fractal search algorithm and cma es |
| topic | optimization algorithm meta-heuristic covariance matrix adaptation evolution strategy stochastic fractal search cec 2017 benchmark problems optimizasyon algoritması meta-sezgisel kovaryans matrisi adaptasyon evrim stratejisi stokastik fraktal arama cec 2017 benchmark problemleri |
| url | https://dergipark.org.tr/tr/download/article-file/2404278 |
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