An improved crow search algorithm for solving numerical optimization functions

作     者：Gholami, Jafar Mardukhi, Farhad Zawbaa, Hossam M. 

作者机构：Islamic Azad Univ Kermanshah Sci & Res Branch Dept Comp Engn Kermanshah Iran Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Technol Univ Dublin Dublin Ireland 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2021年第25卷第14期

页      面：9441-9454页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Enterprise Ireland European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant 

主　　题：Crow search algorithm Meta-heuristic algorithms Numerical optimization functions Swarm intelligence 

摘      要：Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA), which has been tested and evaluated by a set of well-known benchmark functions. A new update mechanism that uses the merits of the global best position to move toward the best position is proposed. This mechanism increases the convergence of the algorithm and improves its local search-ability. Twenty benchmark functions are used to evaluate the performance of the proposed ICSA. Moreover, the ICSA algorithm is compared with the conventional CSA and other meta-heuristic algorithms such as particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), gray wolf optimizer (GWO), moth-flame optimization (MFO), and sine-cosine algorithm (SCA). The experimental result shows that the proposed ICSA algorithm has produced promising results and outperformed conventional CSA and other meta-heuristic algorithms. Also, the proposed ICSA has a more robust convergence for optimizing objective functions in terms of solution accuracy and efficiency.