Echiura Optimization Algorithm

Swarm intelligence optimization algorithms have become an important method for solving complex optimization problems in recent years. This paper proposes a metaheuristic optimization algorithm based on the behavior of marine worms—the Echiura Optimization Algorithm (EOA), and improves upon it to form an adaptive worm chaotic evolutionary algorithm. Inspired by the worms' stretching feeding, burrowing anchoring, and mucus perturbation behaviors, the algorithm achieves a balance between global search and local exploitation through chaotic initialization, Lévy flight, dynamic anchoring coefficients, and cooperative mechanisms. This paper details the algorithm model, iterative formulas, and parameter update mechanism, and analyzes the algorithm's characteristics and potential applications, providing a new bio-inspired approach to swarm intelligence optimization.