Arboreal Monitor Lizard Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the collective behavior of organisms in nature, and have wide applications in many engineering, computer science, and artificial intelligence fields. This paper proposes an optimization algorithm based on the behavioral characteristics of the Arboreal Monitor Lizard (AMLOA). This algorithm simulates the foraging behavior, jumping movement, and energy regulation mechanisms of the AMLOA in the forest canopy, designing a multi-layered sensing global search strategy, a jumping-heuristic local exploitation strategy, and an energy-fitness collaborative adaptive mechanism. The algorithm maintains a good balance between exploration and exploitation in high-dimensional, multi-modal, and complex constraint problems, achieving efficient global optimization. Through analysis of the algorithm flow, mathematical formulas, and characteristics, this paper demonstrates the potential and innovation of the AMLOA in the field of swarm intelligence optimization.