Black-Spotted Lateral-Fold Frog Optimization Algorithm

To address the challenges of nonconvexity, multimodality, and unknown search space structure commonly found in complex continuous optimization problems, a novel swarm intelligence optimization method—the Black-spotted Lateral Fold Frog Optimization Algorithm—is proposed. Inspired by the Black-spotted Lateral Fold Frog's behavior in its natural environment—relying on its lateral folds to sense water flow and vibrations and combining this with its body surface black spots to assess environmental safety—this algorithm introduces new concepts such as lateral fold perception, risk spectrum assessment, and search space folding. In its design, the algorithm no longer assumes a static and uniform geometric structure for the search space; instead, it allows individuals to dynamically deform the search space based on local structural information, thereby guiding adaptive adjustments to the search direction. By constructing a lateral fold fold perception tensor, risk spectrum decision coordinates, and a continuous-state policy mapping mechanism, a natural transition between exploration and development behaviors is achieved. This method theoretically breaks through the limitations of traditional biomimetic optimization algorithms that rely on global optimum traction or fixed update patterns, providing a new research paradigm for geometry-driven optimization algorithms.