Desert Lizard Optimization Algorithm

Swarm optimization algorithms are a class of methods for solving complex optimization problems based on the collective behavior of organisms in nature. This paper proposes a novel swarm optimization algorithm—the Desert Lizard Optimization Algorithm (DLOA). This algorithm simulates the foraging, risk avoidance, and environmental adaptation behaviors of desert lizards in extreme arid environments, introducing multi-scale search, local refinement, dynamic energy regulation, temperature feedback, and random mutation mechanisms. This paper details the individual modeling, behavioral mechanisms, iterative process, and mathematical formulas of the algorithm, and analyzes its multi-scale search capability, resistance to local optima, controllable convergence speed, and robustness. DLOA can be widely applied to continuous optimization, discrete optimization, multi-objective optimization, and dynamic environment optimization problems, providing an efficient and innovative method for complex optimization tasks.