Sloth Rainforest Optimization Algorithm

Swarm optimization algorithms solve complex problems by simulating the behavior of animals in nature. This paper proposes the Sloth (Rainforest) Optimization Algorithm (SROA), inspired by the slow movement, local grasping, neighborhood cooperation, and occasional jumping behavior of sloths in the rainforest, to establish a novel optimization strategy. The algorithm achieves a balance between global search and local exploitation through energy-efficient step size, multi-scale search, local grasping, neighborhood cooperation, and dynamic adjustment of environmental weights. The innovation of SROA lies in the introduction of a neighborhood cooperation mechanism, a hybrid jumping strategy, and adaptive environmental weights, enhancing the algorithm's resistance to local optima and its ability to optimize multimodal functions. This paper systematically describes the algorithm's structure, iterative process, mathematical formulas, characteristic analysis, and potential applications, providing new insights for swarm optimization research.