Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems. (December 2022)

• Record Type: Journal Article
• Authors: Abdollahzadeh, Benyamin; Gharehchopogh, Farhad Soleimanian; Khodadadi, Nima; Mirjalili, Seyedali
• Is Part Of: Advances in engineering software. Volume 174(2022)
• Publication Date: 2022-12
• Subjects: Optimization -- Mountain gazelle optimizer -- Algorithm -- MGO; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103282
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A novel meta-heuristic is proposed inspired by the social life and hierarchy of wild mountain gazelles. Gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm. The proposed algorithm is evaluated and tested using several test beds and case studies. The results demonstrate that the proposed algorithm performs better than the comparable algorithms. Abstract: The Mountain Gazelle Optimizer (MGO), a novel meta-heuristic algorithm inspired by the social life and hierarchy of wild mountain gazelles, is suggested in this paper. In this algorithm, gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm. The MGO algorithm is evaluated and tested using Fifty-two standard benchmark functions and seven different engineering problems. It is compared with nine other powerful meta-heuristic algorithms to validate the result. The significant differences between the comparative algorithms are demonstrated using Wilcoxon's rank-sum and Friedman's tests. Numerous experiments have shown that the MGO performs better than the comparable algorithms on utmost benchmark functions. In addition, according to the tests performed, the MGO maintains its search capabilities and shows good performance even when increasing the dimensions of optimization problems. The source codes of the MGO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/118680-mountain-gazelle-optimizer .