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Using the Novel Wolverine Optimization Algorithm for Solving Engineering Applications

Tareq Hamadneh1, Belal Batiha2, Omar Alsayyed3, Frank Werner4,*, Zeinab Monrazeri5, Mohammad Dehghani5,*, Kei Eguchi6

1 Department of Mathematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Department of Mathematics, Faculty of Science and Information Technology, Jadara University, Irbid, 21110, Jordan
3 Department of Mathematics, Faculty of Science, The Hashemite University, Zarqa, 13133, Jordan
4 Faculty of Mathematics, Otto-von-Guericke University, Magdeburg, 39016, Germany
5 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 71557-13876, Iran
6 Department of Information Electronics, Fukuoka Institute of Technology, Fukuoka, 811-0295, Japan

* Corresponding Authors: Frank Werner. Email: email; Mohammad Dehghani. Email: email

Computer Modeling in Engineering & Sciences 2024, 141(3), 2253-2323. https://doi.org/10.32604/cmes.2024.055171

Abstract

This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired by the foraging behaviors of wolverines in their natural habitats. WoOA innovatively integrates two primary strategies: scavenging and hunting, mirroring the wolverine’s adeptness in locating carrion and pursuing live prey. The algorithm’s uniqueness lies in its faithful simulation of these dual strategies, which are mathematically structured to optimize various types of problems effectively. The effectiveness of WoOA is rigorously evaluated using the Congress on Evolutionary Computation (CEC) 2017 test suite across dimensions of 10, 30, 50, and 100. The results showcase WoOA’s robust performance in exploration, exploitation, and maintaining a balance between these phases throughout the search process. Compared to twelve established metaheuristic algorithms, WoOA consistently demonstrates a superior performance across diverse benchmark functions. Statistical analyses, including paired t-tests, Friedman test, and Wilcoxon rank-sum tests, validate WoOA’s significant competitive edge over its counterparts. Additionally, WoOA’s practical applicability is illustrated through its successful resolution of twenty-two constrained scenarios from the CEC 2011 suite and four complex engineering design challenges. These applications underscore WoOA’s efficacy in tackling real-world optimization challenges, further highlighting its potential for widespread adoption in engineering and scientific domains.

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Cite This Article

APA Style
Hamadneh, T., Batiha, B., Alsayyed, O., Werner, F., Monrazeri, Z. et al. (2024). Using the novel wolverine optimization algorithm for solving engineering applications. Computer Modeling in Engineering & Sciences, 141(3), 2253-2323. https://doi.org/10.32604/cmes.2024.055171
Vancouver Style
Hamadneh T, Batiha B, Alsayyed O, Werner F, Monrazeri Z, Dehghani M, et al. Using the novel wolverine optimization algorithm for solving engineering applications. Comput Model Eng Sci. 2024;141(3):2253-2323 https://doi.org/10.32604/cmes.2024.055171
IEEE Style
T. Hamadneh et al., “Using the Novel Wolverine Optimization Algorithm for Solving Engineering Applications,” Comput. Model. Eng. Sci., vol. 141, no. 3, pp. 2253-2323, 2024. https://doi.org/10.32604/cmes.2024.055171



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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