Forest Dog Optimization Algorithm

Swarm optimization algorithms solve complex optimization problems by simulating the behavior of animal groups in nature, demonstrating strong solution capabilities in continuous, combinatorial, and constrained optimization. However, in high-dimensional, multi-modal, or dynamically changing complex problems, traditional algorithms still suffer from problems such as getting trapped in local optima and unstable convergence speed. This paper proposes a swarm optimization algorithm based on the behavior of forest dogs—the Forest Dog Optimization Algorithm (FDOA). This algorithm simulates the olfactory tracking, vigilant patrolling, and cooperative group behavior of forest dogs in the wild, and establishes a dynamic adaptive model through mathematical formulas. The algorithm achieves a balance between global exploration and local exploitation through multi-scale step size, social learning mechanisms, random mutation strategies, and dynamic weight adjustment, improving its resistance to local optima and convergence efficiency. Theoretical analysis shows that FDOA can effectively converge to the global optimum or near-optimal solution in high-dimensional, multi-modal optimization problems. This paper details the algorithm modeling, iterative process, mathematical formulas, and characteristic analysis, providing new research ideas and application frameworks for swarm optimization algorithms.