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

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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.

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Acknowledgements

Xiao-Zhi Gao’s research work was partially supported by the National Natural Science Foundation of China (NSFC) under Grant 51875113.

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Authors and Affiliations

  1. Centre for Energy, Indian Institute of Technology, Guwahati, India

    Sanchari Deb

  2. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  3. Department of Mechanical Engineering, Aalto University, Espoo, Finland

    Kari Tammi

  4. Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India

    Karuna Kalita & Pinakeswar Mahanta

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  1. Sanchari Deb
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Deb, S., Gao, XZ., Tammi, K. et al. Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018). Artif Intell Rev 53, 1737–1765 (2020). https://doi.org/10.1007/s10462-019-09718-3

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