Open Access

ARTICLE

Using the Novel Wolverine Optimization Algorithm for Solving Engineering Applications

by Tareq Hamadneh¹, Belal Batiha², Omar Alsayyed³, Frank Werner^4,*, Zeinab Monrazeri⁵, Mohammad Dehghani^5,*, 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: ; Mohammad Dehghani. Email:

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

Received 19 June 2024; Accepted 28 August 2024; Issue published 31 October 2024

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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Keywords

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