This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyrax...This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.展开更多
文摘This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.
