Marine Iguana Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the behavioral strategies of organisms in nature. This paper proposes a novel swarm intelligence optimization algorithm—the Marine Iguana Optimization Algorithm (MIOA)—inspired by the foraging behavior, group cooperation mechanisms, and energy regulation strategies of marine iguanas in both land and sea. This algorithm achieves an efficient balance between global search and local exploitation through amphibious behavior patterns, adaptive energy-step size mechanisms, cooperative predation, multi-source perturbation mechanisms, and multi-objective adaptive weights. This paper details the algorithm's mathematical model, iterative update formula, and optimization process, and analyzes its characteristics. Experimental results show that MIOA possesses strong global search capabilities and high local convergence accuracy in both continuous and discrete optimization problems.