A comprehensive survey on symbiotic organisms search algorithms

• Published in: The Artificial intelligence review Vol. 53; no. 3; pp. 2265 - 2312
• Main Authors: Gharehchopogh, Farhad Soleimanian, Shayanfar, Human, Gholizadeh, Hojjat
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2020; Springer; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Heuristic; Heuristic methods; Organic chemistry; Production scheduling; Search algorithms; Statistical analysis; Surveys; Swarm intelligence; SOS algorithms; Meta-heuristic algorithms; Optimization; Nonlinear optimization
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-019-09733-4

Recently, meta-heuristic algorithms have made remarkable progress in solving types of complex and NP-hard problems. So that, most of this algorithms are inspired by swarm intelligence and biological systems as well as other physical and chemical systems in nature. Of course, different divisions for meta-heuristic algorithms have been presented so far, and the number of these algorithms is increasing day by day. Among the meta-heuristic algorithms, some algorithms have a very high efficiency, which are a suitable method for solving real-world problems, but some algorithms have not been sufficiently studied. One of the nature-inspired meta-heuristic algorithms is symbiotic organisms search (SOS), which has been able to solve the majority of engineering issues so far. In this paper, firstly, the primary principles, the basic concepts, and mathematical relations of the SOS algorithm are presented and then the engineering applications of the SOS algorithm and published researches in different applications are examined as well as types of modified and multi-objective versions and hybridized discrete models of this algorithm are studied. This study encourages the researchers and developers of meta-heuristic algorithms to use this algorithm for solving various problems, because it is a simple and powerful algorithm to solve complex and NP-hard problems. In addition, a detailed and perfect statistical analysis was performed on the studies that had used this algorithm. According to the accomplished studies and investigations, features and factors of this algorithm are better than other meta-heuristic algorithm, which has increased its usability in various fields.