Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems

Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Nima Khodadadi, Seyedali Mirjalili

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number103282
JournalAdvances in Engineering Software
Volume174
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Algorithm
  • MGO
  • Mountain gazelle optimizer
  • Optimization

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