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

Research output: Contribution to journalReview ArticleScientificpeer-review

Researchers

  • Sanchari Deb
  • Xiao Zhi Gao
  • Kari Tammi

  • Karuna Kalita
  • Pinakeswar Mahanta

Research units

  • Indian Institute of Technology, Guwahati
  • University of Eastern Finland

Abstract

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.

Details

Original languageEnglish
Number of pages29
JournalArtificial Intelligence Review
Publication statusE-pub ahead of print - 23 May 2019
MoE publication typeA2 Review article in a scientific journal

    Research areas

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

ID: 36987040