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https://hdl.handle.net/11499/47360
Title: | A penalty-based algorithm proposal for engineering optimization problems | Authors: | Oztas G.Z. Erdem S. |
Keywords: | Constrained nonlinear optimization Engineering benchmark problems Evolutionary computation Metaheuristics Natural facts Nature-inspired optimization algorithms Benchmarking Biomimetics Constrained optimization Constraint satisfaction problems Nonlinear programming Tabu search Benchmark problems Computation model Constrained nonlinear optimization Engineering benchmark problem Engineering optimization problems Metaheuristic Natural fact Nature-inspired optimization algorithm Nonlinear optimization problems Optimization algorithms Evolutionary algorithms |
Publisher: | Springer Science and Business Media Deutschland GmbH | Abstract: | This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. | URI: | https://doi.org/10.1007/s00521-022-08058-8 https://hdl.handle.net/11499/47360 |
ISSN: | 0941-0643 |
Appears in Collections: | İktisadi ve İdari Bilimler Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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s00521-022-08058-8.pdf | 1.77 MB | Adobe PDF | View/Open |
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