Spotted Deer Optimization Algorithm

Swarm intelligence optimization algorithms demonstrate excellent global search capabilities and robustness in solving complex optimization problems. Based on observations and abstractions of sika deer ecological behavior, this paper proposes a Spotted Deer Optimization Algorithm (SDOA). This algorithm transforms sika deer behaviors such as seasonal migration, social status formation, arena competition, fawn learning, and pheromone regulation into a mathematical model to achieve a dynamic balance between global and local search. The paper first constructs a mathematical description of the sika deer population and a search strategy formula. Then, mathematical expressions for individual migration, status renewal, energy modeling, fawn perturbations, and pheromone regulation are derived in detail. Finally, complexity analysis and convergence proof demonstrate the theoretical feasibility and effectiveness of the algorithm.