Tapir Mouse Optimization Algorithm

Swarm intelligence optimization algorithms are important tools for solving complex optimization problems. This paper proposes a swarm intelligence optimization algorithm based on tapir behavior (Tapir Mouse Optimization Algorithm, TMOA). This algorithm achieves an effective balance between global exploration and local exploitation of the search space by simulating the foraging behavior, local exploration ability, and long-distance jumping behavior of tapirs in their natural environment. The algorithm achieves efficient search for complex high-dimensional optimization problems by introducing environmental adaptation mechanisms, social learning mechanisms, multi-energy level models, and dynamic search mode switching strategies. This paper elaborates on the design concept and mathematical mechanism of TMOA from five aspects: algorithm model, iterative process, mathematical formulas, characteristic analysis, and application extensions, fully demonstrating its advantages in global search capability, local convergence speed, and algorithm robustness.