European Green Toad Optimization Algorithm

To address the common problems of premature convergence, collective cognitive collapse, and the disconnect between exploration and exploitation mechanisms in existing swarm optimization algorithms for complex, noisy, and multimodal optimization problems, this paper proposes a novel swarm optimization method—the European Green Toad Optimization Algorithm. This algorithm no longer views the optimization process as a continuous movement of individuals within the objective function space, but rather models it as an ecological decision-making process driven by "survival sustainability." The algorithm constructs a local survival sustainability function to characterize the rationality of individuals continuing their search in the current region; it introduces an information toxicity field to suppress homogenization in search behavior; and it further interprets the search dynamics as an ecological phase transition process consisting of stable, metastable, and collapsed states. Experimental and theoretical analyses demonstrate that this method exhibits stronger robustness and diversity preservation capabilities in complex search environments, providing a new modeling perspective for swarm optimization.