South American Tapir Optimization Algorithm

Swarm intelligence optimization algorithms are a class of optimization methods inspired by the collective behavior of groups in nature, and are widely used in continuous, discrete, and combinatorial optimization problems. This paper proposes a novel swarm intelligence optimization algorithm—the Tapir Optimization Algorithm (STAO). Modeled on the foraging behavior of tapirs in tropical rainforests, it combines olfactory guidance, environmental perception and cooperation, dynamic step size, and random jump mechanisms to achieve a balance between efficient global search and local exploitation. This paper provides a mathematical model of the algorithm, systematically analyzes its multi-scale search capability, resistance to local optima, adaptive convergence characteristics, and swarm cooperation mechanism, and explores its application potential in continuous, discrete, and hybrid optimization. All mathematical formulas are expressed in plain text to ensure clear and complete theoretical descriptions. Results show that STAO has significant theoretical innovation and broad application prospects.