Awash Mastomys: A Novel Metaheuristic Algorithm Inspired by the Foraging Behavior of *Mastomys awashicus*

This paper presents Awash Mastomys, a new metaheuristic optimization algorithm inspired by the foraging strategies of *Mastomys awashicus*, a small rodent found in Ethiopia. *Mastomys awashicus* exhibits remarkable efficiency in locating and exploiting food sources in challenging environments. This paper details the algorithm's design, incorporating key behavioral traits such as adaptive searching, risk aversion, and memory utilization. Awash Mastomys leverages a novel combination of particle swarm optimization (PSO) and a simulated annealing (SA) framework, incorporating a dynamic food source representation and a probabilistic decision-making process. The algorithm is evaluated on a range of benchmark optimization problems, demonstrating competitive performance compared to established metaheuristics. The core of the algorithm revolves around the concept of "food patches," representing potential solutions, and the dynamic adjustment of the probability of exploring new patches versus exploiting known, promising ones. The algorithm's parameters are carefully tuned to balance exploration and exploitation, resulting in robust and efficient optimization capabilities. The paper concludes with a discussion of the algorithm's strengths, limitations, and potential future research directions.