To address the limitations of traditional optimization methods in achieving high accuracy in high-dimensional problems, this paper introduces the snow leopard optimization (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by the territorial behaviors of snow leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, SLO achieves a balance between exploration and exploitation, to navigate vast and complex search spaces. The algorithm’s performance was evaluated using the CEC2017 benchmark and high-dimensional genetic data feature selection tasks, demonstrating SLO’s competitive advantage in solving high-dimensional optimization problems. In the CEC2017 experiments, SLO ranked first in the Friedman test, outperforming several well-known algorithms, including ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, and HHO. The effective application of SLO in high-dimensional genetic data feature selection further highlights its adaptability and practical utility, marking significant progress in the field of high-dimensional optimization and feature selection.

This paper introduces a novel meta-heuristic algorithm know as Rhinopithe-cus Swarm Optimization (RSO) to deal with the high-dimensional problems. The RSO is inspired by the different social behaviors of different subgroups in the rhinopithecus swarm. This algorithm categorizes the population into king, matures, adolescences and infancy based on fitness values before each iteration. At iterations they are given different search methods including vertical migration , concerted search and mimicry due to their social division of labor. The experiment was independently conducted 36 times on the highest dimension recommended by CEC2017. The results show that comprehensive performance of RSO is higher than 8 well-known meta-heuristic algorithms including DBO, BWO, SSA, AVOA, WOA, ARBBPSO, GTO, and HHO. RSO ranked first in the CEC2017 with a score of 1.655 on 29 benchmark functions using Ferideman test, which is 42.8% better than the second-ranked algorithm SSA. Overall, it 1 is concluded that RSO outperforms well-known algorithms and can better solve high-dimensional optimization problems