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

Tutkimustuotos: Lehtiartikkeli


  • Sanchari Deb
  • Xiao Zhi Gao
  • Kari Tammi

  • Karuna Kalita
  • Pinakeswar Mahanta


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


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.


JulkaisuArtificial Intelligence Review
TilaSähköinen julkaisu (e-pub) ennen painettua julkistusta - 23 toukokuuta 2019
OKM-julkaisutyyppiA2 Arvio tiedejulkaisuussa (artikkeli)

ID: 36987040