A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm Optimization
Guoyuan Zhou, Dong Wang, Guoao Zhou, Jiaxuan Du, Jia Yuan Guo · Research Square · 2024
Abstract 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