Hamster Optimization Algorithm

In recent years, optimization algorithms based on natural biological behaviors have been widely used in solving complex optimization problems, such as ant colony optimization, particle swarm optimization, and wolf pack optimization. This paper proposes a novel heuristic optimization algorithm—the Hamster Optimization Algorithm (HOA). Based on the foraging, storage, alertness, and social interaction behaviors of hamsters, this algorithm achieves efficient global exploration and local refinement of complex optimization problems by constructing a multi-behavior fusion mechanism, a dynamic weight adjustment mechanism, and a nonlinear perturbation mechanism. This paper systematically derives the mathematical model of the algorithm and describes the algorithm flow and parameter design in detail. Experimental results show that HOA outperforms traditional optimization algorithms in convergence speed and optimization accuracy on the standard test function, exhibiting good global search capabilities and the ability to avoid local optima, providing a new method for solving practical engineering optimization problems.