Advances in Sparrow Search Algorithm: A Comprehensive Survey

作     者：Gharehchopogh, Farhad Soleimanian Namazi, Mohammad Ebrahimi, Laya Abdollahzadeh, Benyamin 

作者机构：Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Islamic Azad Univ Dept Comp Engn Maybod Branch Maybod Iran 

出 版 物：《ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING》 (工程计算方法纪要)

年 卷 期：2023年第30卷第1期

页      面：427-455页

核心收录：

学科分类：08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Mathematical programming 

摘      要：Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.