Northern Pike Optimization Algorithm

To address the problems of premature convergence, local convergence instability, and repetitive searches in traditional swarm intelligence algorithms for high-dimensional complex multimodal optimization problems, this paper proposes a pike-fish optimization algorithm based on topological energy fields and failure memory barrier mechanisms. The algorithm constructs an information refraction field as a continuous guiding potential field to avoid premature convergence caused by single-point optimal attraction; it achieves dimensional adaptive step size adjustment through anisotropic visual tensors; it introduces energy accumulation and curvature response mechanisms to achieve compressed manifold attack behavior; it constructs swarm order parameters and designs a phase transition mechanism to enhance global exploration capabilities; it establishes a failure memory barrier field to achieve irreversible region suppression; and finally, it constructs Lyapunov-type functions to analyze the algorithm's stability. This method models the swarm search process as a coupled dynamical system driven by a nonlinear energy field, providing an interpretable theoretical framework for swarm intelligence optimization.