Flower Mouse Optimization Algorithm

Optimization problems have wide applications in scientific research, engineering design, and artificial intelligence. Traditional optimization algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, are prone to getting stuck in local optima or having slow convergence speeds in certain complex problems. This paper proposes a novel optimization algorithm based on the behavioral characteristics of chipmunks—the Flower Mouse Optimization Algorithm (FMOA). This algorithm simulates the foraging, risk avoidance, cooperation, and reproduction behaviors of chipmunks in their natural environment, achieving a balance between global and local search. FMOA introduces multi-level behavioral mechanisms, risk avoidance mechanisms, information sharing mechanisms, and adaptive environmental feedback mechanisms. It models the individual chipmunk position updates, energy updates, and group cooperation using mathematical formulas, ensuring that the algorithm has high convergence speed and solution diversity in complex optimization problems. Experimental results show that FMOA exhibits good global search capability and local convergence accuracy in multidimensional continuous optimization problems.