Gray Squirrel Foraging Algorithm for Function Optimization

In this study, a novel and efficient metaheuristic algorithm inspired by the foraging behavior of gray squirrels is proposed to tackle complex optimization problems. Similar to many nature-inspired algorithms, the Gray Squirrel Foraging Algorithm (GSFA) is population-based and explores the search space using a set of initial solutions, gradually converging toward the global optimum. The search mechanism is modeled on the natural behavior of squirrels in locating and retrieving hidden food sources and has been implemented in MATLAB. GSFA employs three distinct search strategies to balance exploration and exploitation: (1) directional search around prominent elements such as large trees that serve as food storage sites, (2) triangulation-based search utilizing environmental landmarks such as bushes and rocks, and (3) random search guided by olfactory cues. This multi-strategy framework enhances global search capabilities and prevents premature convergence. Moreover, candidate solutions are continuously evaluated based on their quality, allowing even weaker solutions a chance to improve and contribute to the overall search process. This feature reduces computational cost and accelerates convergence. The proposed algorithm has been evaluated on eight standard benchmark functions and demonstrated superior performance compared to several well-known metaheuristic algorithms. Due to its adaptive mechanism and computational efficiency, GSFA holds significant potential for application in a wide range of real-world optimization problems, particularly in the field of engineering.