Seadragon Optimizer Algorithm

Swarm intelligence optimization algorithms, as important methods for solving complex optimization problems, have been widely applied in scientific computing, engineering optimization, and machine learning in recent years. This paper proposes a novel swarm intelligence optimization algorithm—the Seadragon Optimizer Algorithm (SDA), inspired by the cooperative, camouflage, and predatory behaviors of the sea swarm (Solenostomus paradoxus) in its natural environment. By establishing individual position representations, an energy model, a cooperative mechanism, and multi-strategy perturbations, the algorithm effectively combines global search with local fine-grained search. The algorithm is energy-driven at its core, employing dynamic step size, adaptive neighbor cooperation, and multi-perturbation strategies to ensure both rapid exploration of potential regions and precise localization of the global optimum within the search space. This paper provides a detailed analysis of the algorithm's mathematical model, iterative mechanism, and characteristics, offering a new design approach for swarm intelligence optimization algorithms.