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

The Mountain Gazelle Optimizer (MGO), a novel meta-heuristic algorithm inspired by the social life and hier-archy 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/1186 80-mountain-gazelle-optimizer.

Automated summary

This paper proposes a novel meta-heuristic algorithm called the Mountain Gazelle Optimizer (MGO), which is inspired by the social life and hierarchy of wild mountain gazelles. The MGO algorithm formulates the hierarchical and social life of gazelles mathematically to develop an optimization algorithm. It is evaluated and tested using standard benchmark functions and engineering problems, and compared with other meta-heuristic algorithms to validate its effectiveness. The experiments show that the MGO performs better than the comparable algorithms and maintains good performance even when increasing problem dimensions.