Camel Mouse Optimization Algorithm

Swarm optimization algorithms, by simulating the collective behavior of organisms in nature, demonstrate excellent search capabilities in complex optimization problems. This paper proposes an optimization algorithm based on the behavior of the desert camel mouse—the Camel Mouse Optimization Algorithm (CMOA). This algorithm simulates the foraging, water storage, jumping, and cooperative behaviors of camels in extreme desert environments, introducing energy-driven, adaptive step size, historical memory, and multi-scale jumping mechanisms to achieve a dynamic balance between global exploration and local exploitation. This paper details the algorithm's modeling, iterative process, mathematical mechanisms, and characteristic analysis, and discusses its application prospects in high-dimensional optimization, combinatorial optimization, engineering design, and machine learning parameter optimization. Results show that CMOA possesses strong resistance to local optima, convergence stability, and adaptability to complex environments.