Sumatran Rhinoceros Optimization Algorithm

In recent years, swarm intelligence optimization algorithms have been widely applied in complex high-dimensional optimization problems, such as particle swarm optimization, ant colony optimization, and whale optimization. However, traditional algorithms still have shortcomings in global exploration ability, local convergence speed, and the ability to escape local optima. This paper proposes an optimization algorithm based on the behavioral characteristics of Sumatran rhinoceros—the Sumatran Rhinoceros Optimization Algorithm (SROA). This algorithm simulates the foraging, exploration, and cooperative behavior of rhinoceroses in nature, achieving an effective balance between global exploration and local exploitation through olfactory dynamic adaptation, force-driven breakthroughs, multi-level memory pools, heterogeneous group cooperation, and random perturbation mechanisms. This paper provides a detailed mathematical model and formulaic description of the algorithm, including group initialization, exploration behavior, local exploitation, olfactory adaptive adjustment, random perturbation, and multi-level memory pool mechanisms. Through analysis, SROA possesses significant global search ability, local fine-tuning ability, and strong ability to escape local optima, making it suitable for high-dimensional complex optimization problems.