Water Monitor Lizard Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the collective behavior of organisms in nature. Traditional algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization (WOA) suffer from a trade-off between global search and local exploitation, easily getting trapped in local optima or experiencing slow convergence. This paper proposes a novel optimization algorithm based on the behavioral characteristics of the Water Monitor Lizard—the Water Monitor Lizard Optimization Algorithm (WMLOA). This algorithm achieves intelligent search by simulating the environmental perception capabilities, policy flexibility, and swarm cooperation of the Water Monitor Lizard. The algorithm design includes a dynamic environmental perception mechanism, a hybrid behavior strategy, adaptive weight adjustment, historical trajectory reflection, and a swarm cooperation mechanism, which enhances local exploitation efficiency while ensuring global exploration capabilities and improving the ability to escape local optima. This paper provides a detailed description of the algorithm modeling, mathematical formulas, and algorithm characteristics, offering a new method for high-dimensional continuous optimization, multi-peak optimization, and combinatorial optimization problems.