Sea Lily Optimization Algorithm

Swarm intelligence optimization algorithms have wide applications in continuous function optimization, combinatorial optimization, and complex engineering design problems. This paper proposes an optimization algorithm based on the foraging behavior of crinoids—the Sea Lily Optimization Algorithm (SLOA). This algorithm simulates the "swing-extension-contraction" behavior of crinoids in ocean current environments and introduces energy cooperation mechanisms, density regulation mechanisms, and adaptive weight update strategies to improve the algorithm's global search capability and local optimization accuracy. This paper systematically analyzes the algorithm's mathematical model, iterative mechanism, and theoretical characteristics, and demonstrates the algorithm's convergence and diversity maintenance principles through formulas, providing an effective solution method for complex optimization problems.