Oceanic Polychaeta Optimization Algorithm

Swarm intelligence optimization algorithms are a class of methods that solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a novel optimization algorithm based on the behavior of marine polychaetes—the Marine Polychaete Optimization Algorithm (OMPOA). This algorithm simulates the peristaltic foraging, mucus local anchoring, and collective cooperative behavior of polychaetes on the seabed. Through an adaptive peristaltic mechanism, a combination of spiral peristalsis and Lévy flight perturbation, local exploitation with dynamic mucus weights, adjustment of group information entropy, and a multi-group asynchronous cooperative mechanism, it achieves efficient global search and local exploitation. The algorithm design features multi-scale search capability, strong resistance to local optima, and high convergence stability, and can be widely applied to high-dimensional optimization, combinatorial optimization, and engineering design problems.