Unicorn Beetle Optimization Algorithm: A Horn-Driven Nonlinear Momentum Search Mechanism for Global Optimization

To address the problems of traditional swarm intelligence algorithms susceptibility to local optima, insufficient isotropic orientation search, and oscillations in the later stages of convergence in complex high-dimensional nonconvex optimization problems, this paper proposes an optimization algorithm based on the dynamic mechanism of unicorn beetle behavior. This algorithm unifies impact behavior, orientation deflection, energy accumulation, and curvature adjustment mechanisms within a nonlinear discrete dynamic system framework. By constructing an anisotropic impact tensor, an adaptive rotation angle dynamic equation, an energy conservation-driven burst mechanism, and a curvature-sensing scale adjustment model, the algorithm acquires adaptive orientation adjustment capabilities and stable convergence characteristics. The overall update form of the algorithm is a nonlinear iterative system with multiple coupled mechanisms. This paper systematically elaborates on the model construction, mathematical expression, dynamic interpretation, and complexity analysis, and provides a unified update equation structure, laying the foundation for subsequent theoretical analysis and engineering applications.