Desert Fox Optimization Algorithm

Swarm intelligence optimization algorithms are a class of computational methods based on the collective behavior of organisms in nature, and have been widely applied in engineering optimization, data mining, and artificial intelligence in recent years. This paper proposes a novel optimization algorithm based on the behavioral characteristics of the desert fox—the Desert Fox Optimization Algorithm (DFOA). This algorithm simulates the foraging, hunting, and energy-saving behaviors of the desert fox in extreme environments. Through a multi-stage search mechanism, cooperative hunting strategy, adaptive energy management, and parameter tuning, it achieves a balance between global exploration and local exploitation. This paper provides a detailed analysis of the algorithm model, iterative process, and mathematical formulas, and discusses the algorithm's characteristics in terms of global search capability, local exploitation capability, resistance to local optima, and scalability. Research shows that DFOA has strong adaptability to multimodal problems and the ability to solve high-dimensional optimization problems, providing an efficient and flexible solution to complex optimization problems.