Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

作     者：Belal Al-Khateeb Kawther Ahmed Maha Mahmood Dac-Nhuong Le 

作者机构：College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq General Directorate of Scientific WelfareMinistry of Youth and SportBaghdadIraq Institute of Research and DevelopmentDuy Tan UniversityDanang550000Vietnam Faculty of Information TechnologyDuy Tan UniversityDanang550000Vietnam 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第7期

页      面：643-654页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Optimization metaheuristic constrained optimization rock hyraxes swarm optimization RHSO 

摘      要：This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in *** RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this *** hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the ***-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 ***,RHSO is very effective in solving real issues with constraints and new search *** 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.