Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social n...

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Veröffentlicht in:The Artificial intelligence review Jg. 53; H. 3; S. 1737 - 1765
Hauptverfasser: Deb, Sanchari, Gao, Xiao-Zhi, Tammi, Kari, Kalita, Karuna, Mahanta, Pinakeswar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.03.2020
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Biology
Birds
Computer Science
Distributed generation
Feature selection
Food
Foraging behavior
Genetic algorithms
Heuristic
Heuristic methods
Mathematical optimization
Mechanical engineering
Meta-analysis
Optimization
Optimization algorithms
Poultry
Route planning
Social networks
Unit commitment
Variants
Vehicle routing
Optimization algorithm
Review
Nature inspired intelligence
Applications
Chicken Swarm Optimization algorithm
ISSN:0269-2821, 1573-7462
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Zusammenfassung:Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-019-09718-3