Desert Giant Lizard Optimization Algorithm

Swarm intelligence optimization algorithms are a class of optimization methods based on the simulation of biological behavior in nature, and have been widely applied to problems such as function optimization, combinatorial optimization, and high-dimensional optimization in recent years. This paper proposes a novel swarm intelligence optimization algorithm—the Desert Monitor Lizard Optimization Algorithm (DGLOA), which simulates the foraging, predator avoidance, territorial patrol, and energy regulation behaviors of desert monitor lizards in extreme environments. The algorithm achieves efficient search for complex optimization problems through individual multi-scale global foraging, neighborhood learning for local exploitation, group cooperation guidance, historical memory reference, and adaptive energy regulation. The paper provides a systematic mathematical model of the algorithm, presents the complete iterative process and formulas, analyzes the algorithm's characteristics and convergence performance, and explores its application potential in continuous optimization, discrete optimization, and dynamic environments. Research shows that DGLOA possesses both global exploration and local exploitation capabilities, exhibiting strong resistance to local optima and adaptive search capabilities.