Chaotic opposition Golden Sinus Algorithm for global optimization problems
Article
Olmez, Yagmur, Koca, Gonca Ozmen, Sengur, Abdulkadir and Acharya, U. Ranjendra. 2024. "Chaotic opposition Golden Sinus Algorithm for global optimization problems." Chaos, Solitons and Fractals. 183. https://doi.org/10.1016/j.chaos.2024.114869
Article Title | Chaotic opposition Golden Sinus Algorithm for global optimization problems |
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ERA Journal ID | 88 |
Article Category | Article |
Authors | Olmez, Yagmur, Koca, Gonca Ozmen, Sengur, Abdulkadir and Acharya, U. Ranjendra |
Journal Title | Chaos, Solitons and Fractals |
Journal Citation | 183 |
Article Number | 114869 |
Number of Pages | 22 |
Year | 2024 |
Publisher | Elsevier |
ISSN | 0960-0779 |
1873-2887 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.chaos.2024.114869 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0960077924004211 |
Abstract | Optimization techniques are required to find the best solutions to challenging problems in many engineering disciplines. Metaheuristic algorithms that effectively increase search performance using various evolutionary strategies have become increasingly popular in recent years. The Golden Sinus Algorithm (GoldSa) is a population-based optimization algorithm that uses the sine function and the golden ratio. In this study, a chaotically enhanced opposition-based Golden Sinus Algorithm (Co-GoldSa) has been proposed to improve the efficiency of the exploitation and exploration ability of the GoldSa method. While designing this approach, it is first necessary to analyze the chaotic behavior of the GoldSa parameters. For this purpose, three chaotic GoldSa methods have been developed using eight chaotic maps to determine the effect of the chaotic maps on the parameters with different behaviors. Secondly, the opposition-based learning strategy is adjusted to the cGoldSa to enhance the searching ability. To investigate the proficiency of the proposed Co-GoldSa method, it has been examined with well-known and newly introduced metaheuristic approaches on benchmark functions and classical engineering design problems. Besides, an efficient framework of the multilevel thresholding image segmentation has been presented based on the Co-GoldSa method since the efficient processing of pathological images is quite important in medical diagnostics. The experimental outcomes reveal the superiority of the proposed method in solving global optimization problems, image segmentation, and engineering problems. Thus, the outcomes of the benchmark functions, image segmentation, and classical engineering problems support that the proposed Co-GoldSa approach can be considered a promising method for resolving challenging optimization problems. |
Keywords | Breast cancer; Golden Sinus Algorithm ; Multilevel thresholding ; Chaos theory ; Opposition-based learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460203. Evolutionary computation |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Firat University, Turkey |
School of Mathematics, Physics and Computing |
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