Bluegill Sunfish Optimization Algorithm

Swarm intelligence optimization algorithms have been widely applied in high-dimensional complex nonlinear optimization problems in recent years. However, most bio-inspired algorithms remain at the level of simple behavior mapping, lacking a unified dynamic system structure and macroscopic control mechanism. This paper proposes an optimization algorithm for bluegill sunfish based on the concept of dynamic ecological energy fields. Starting from the ecological group behavior, the algorithm introduces a deformable nest potential field model, a continuous role manifold mapping mechanism, a curvature-driven refraction search mechanism, an asymmetric game substitution mechanism, and an information entropy control mechanism to construct a multi-field coupled discrete dynamic system. This paper presents the complete mathematical model, the dynamic system expression, and the Lyapunov stability analysis framework. Theoretical analysis shows that the algorithm can achieve asymptotic convergence while maintaining population diversity, providing a swarm intelligence model with physical interpretation for complex nonconvex optimization problems.