Social group optimization: a-state-of-the-art review

Human behaviour-based meta-heuristic optimization algorithms have been prominent in the field of optimization and have gained significance with their simplicity, robust performance, relatively low number of algorithm-specific parameters and ease of implementation to multi-disciplinary problems compa...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 84; no. 30; pp. 37215 - 37310
Main Authors: Reddy, Aala Kalananda Vamsi Krishna, Narayana, Komanapalli Venkata Lakshmi
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2025
Springer Nature B.V
Subjects:
Algorithms
Communications systems
Computer Communication Networks
Computer Science
Convergence
Critical path
Data Structures and Information Theory
Engineering
Entrapment
Exploitation
Foraging behavior
Genetic algorithms
Heuristic
Heuristic methods
Improvisation
Multimedia Information Systems
Optimization
Optimization techniques
Paradigms
Parameter robustness
Researchers
Robustness
Special Purpose and Application-Based Systems
State-of-the-art reviews
Teaching
Tuning
Human behaviour based-meta heuristic optimization
Modified Social Group Optimization (MSGO)
Social Group Optimization (SGO)
Greedy selection
Acquiring phase
Applications of SGO
Improved Social Group Optimization (ISGO)
Review of SGO
Multi-objective Social Group Optimization (MOSGO)
Improving phase
ISSN:1573-7721, 1380-7501, 1573-7721
Online Access:Get full text
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Summary:Human behaviour-based meta-heuristic optimization algorithms have been prominent in the field of optimization and have gained significance with their simplicity, robust performance, relatively low number of algorithm-specific parameters and ease of implementation to multi-disciplinary problems compared to other optimization techniques. One such meta-heuristic is the Social Group Optimization (SGO) algorithm, which is inspired by the collaboration of human beings through the manipulation of their behavioural traits towards solving complex problems. Proposed in 2016, SGO has gained a lot of attention with its simple realization, a single tuning parameter and robust performance across various disciplines. SGO has shown better performance in terms of exploration, exploitation and faster convergence and has proven capable of handling complex optimization problems without the problems of the curse of dimensionality, local entrapment and premature convergence. This article analyses the development of SGO and its working and outlines the significance of SGO. The various phases in the algorithm its benchmarking performance and tuning are analyzed in detail. This is followed by a comprehensive review of its applications in the fields of engineering, computer science, medicine, communication systems and finance. The application of SGO and its performance have been examined and analyzed. The improvements corresponding to SGO i.e., variants of SGO are analyzed and reviewed.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-025-20607-6