A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems
This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conduct...
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| Vydané v: | Journal of computational design and engineering Ročník 9; číslo 6; s. 2452 - 2465 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Oxford University Press
01.12.2022
한국CDE학회 |
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| ISSN: | 2288-5048, 2288-4300, 2288-5048 |
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| Abstract | This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.
Graphical Abstract
Graphical Abstract |
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| AbstractList | This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.
Graphical Abstract
Graphical Abstract This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models. This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models. KCI Citation Count: 37 |
| Author | Yildiz, Ali Riza Yıldız, Betül Sultan Panagant, Natee Mehta, Pranav Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Betül Sultan surname: Yıldız fullname: Yıldız, Betül Sultan – sequence: 2 givenname: Pranav surname: Mehta fullname: Mehta, Pranav – sequence: 3 givenname: Natee surname: Panagant fullname: Panagant, Natee – sequence: 4 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali – sequence: 5 givenname: Ali Riza surname: Yildiz fullname: Yildiz, Ali Riza email: aliriza@uludag.edu.tr |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002914498$$DAccess content in National Research Foundation of Korea (NRF) |
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| ContentType | Journal Article |
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| Keywords | Runge Kutta optimization algorithm hybrid metaheuristics brake pedal chaotic maps mechanical design |
| Language | English |
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