Quadruped Lizard Optimization Algorithm

Swarm intelligence optimization algorithms are a class of methods that solve complex optimization problems by simulating the collective behavior of biological groups in nature. They have been widely applied in function optimization, engineering design, and hyperparameter tuning in machine learning. This paper proposes a novel swarm intelligence optimization algorithm—the Quadruped Lizard Optimization Algorithm (QLOA). The algorithm achieves a balance between global search and local exploitation by simulating the foraging, escape, tail fine-tuning, and social cooperation behaviors of quadruped lizards in nature. This paper provides a detailed analysis of QLOA from a mathematical modeling perspective, including the individual position update formula, limb dynamic step size, tail perturbation mechanism, environmentally sensitive energy adjustment, and multi-source social decision-making mechanism. The results show that the algorithm theoretically possesses high global search capability, fine-tuned local exploitation, dynamic adaptability, and strong group cooperation, providing a new solution tool for complex optimization problems.