Yellow Ground Squirrel Algorithm (YGSA): A Novel Metaheuristic Algorithm for Global Optimization

Abstract

In recent years, numerous methods have emerged for optimizing problems inspired by nature. Each of these methods, according to their nature, has applications for solving specific problems with unique characteristics. Given the wide array of optimization problems across various fields, introducing novel algorithms with distinct behaviors can be both appealing and practical. In this article, a nature-inspired meta-heuristic optimization algorithm called Yellow Ground Squirrel Algorithm (YGSA) is introduced. The main idea behind YGSA is to simulate the natural behavior of a yellow ground squirrel in terms of pursuit and evasion from a farmer (the predator) and reaching the nest. The objective of the proposed algorithm is to enhance and attain a more equitable equilibrium between the fundamental stages of exploration and exploitation in contrast to preceding approaches. This balance arises from the fact that the yellow ground squirrel, during its escape, simultaneously tries to increase the distance from the farmer (the predator) and decrease the distance to its nest (burrow). To validate the proposed algorithm, 56 benchmark evaluation functions from various unimodal and multimodal types have been evaluated. The evaluation results are compared with the HBO, EBO-CMAR, FO, SCA, PSO, CS, GSA, MVO, and WSO algorithms. The optimization results for unimodal test functions demonstrate the high exploitation capability of YGSA in approaching the optimal solution, while the optimization results for multimodal test functions indicate the strong exploration ability of YGSA in finding the main optimal region in the search space.