Marine Scale Worm 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 swarm intelligence optimization algorithm—the Marine Scale Worm Optimization Algorithm (MSWOA)—inspired by the foraging, swimming, and defense behaviors of deep-sea marine swarms. The algorithm performs joint optimization through global exploration and local exploitation by considering the energy, swimming speed, scale coefficient, and diversity index of individual organisms. It also introduces an adaptive scale vibration mechanism, neighbor difference guidance, and energy-velocity coupling strategy, giving the algorithm multi-scale search capabilities, strong resistance to local optima, and high convergence. This paper systematically elaborates on the algorithm's modeling, iterative process, characteristic analysis, and application extensions, and describes the core mechanism of the algorithm in detail using mathematical formulas, providing a novel and effective solution method for high-dimensional complex optimization problems.