Symbiotic organisms search algorithm: Theory, recent advances and applications

•Exhaustive study of the classical symbiotic organisms search algorithm.•A systematic review of symbiotic organisms search algorithm variants and hybrid algorithms.•Presentation of the various application areas of SOS algorithm.•Suggestions for further development and possible areas of improvement.•...

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Bibliographic Details
Published in:Expert systems with applications Vol. 119; pp. 184 - 209
Main Authors: Ezugwu, Absalom E., Prayogo, Doddy
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.04.2019
Elsevier BV
Subjects:
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•Exhaustive study of the classical symbiotic organisms search algorithm.•A systematic review of symbiotic organisms search algorithm variants and hybrid algorithms.•Presentation of the various application areas of SOS algorithm.•Suggestions for further development and possible areas of improvement.•Recommendations for novel application areas for the SOS algorithm. The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.10.045