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

Sanchari Deb*, Xiao Zhi Gao, Kari Tammi, Karuna Kalita, Pinakeswar Mahanta

*Corresponding author for this work

Research output: Contribution to journalReview ArticleScientificpeer-review

2 Citations (Scopus)
12 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1737-1765
Number of pages29
JournalArtificial Intelligence Review
Issue number3
Early online date23 May 2019
Publication statusPublished - 1 Mar 2020
MoE publication typeA2 Review article in a scientific journal


  • Applications
  • Chicken Swarm Optimization algorithm
  • Nature inspired intelligence
  • Optimization algorithm
  • Review

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