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Flower pollination algorithm development: a state of art review

Author

Listed:
  • Sangeeta Pant

    (University of Petroleum & Energy Studies)

  • Anuj Kumar

    (University of Petroleum & Energy Studies)

  • Mangey Ram

    (Graphic Era University)

Abstract

The journey of a modern man from a troglodyte is due to human nature to try to unfold the mysteries of nature to improve the lives of human beings. A few years back we even can’t think that school of fish, genes, nature of bat or ant can be used to design optimization algorithms. As nature has the solution of every problem. Researchers working on optimization theory are developing optimization techniques which are inspired by nature and could be utilized as optimization tools for engineering problems. Recently, flower pollination algorithm, which is inspired by the pollination characteristics of flowering plants and associated flower constancy of some pollinating insects, caught the eye of many researchers in the world of optimization. This paper presents a brief review about the algorithm its developments and applications. In the last part of this paper, the authors have listed the limitations and topics within FPA that the authors consider as promising areas of future research.

Suggested Citation

  • Sangeeta Pant & Anuj Kumar & Mangey Ram, 2017. "Flower pollination algorithm development: a state of art review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1858-1866, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-017-0623-7
    DOI: 10.1007/s13198-017-0623-7
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    References listed on IDEAS

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    1. Dubey, Hari Mohan & Pandit, Manjaree & Panigrahi, B.K., 2015. "Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch," Renewable Energy, Elsevier, vol. 83(C), pages 188-202.
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    Cited by:

    1. Dhivya Swaminathan & Arul Rajagopalan, 2022. "Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    2. Stephen Afrifa & Tao Zhang & Peter Appiahene & Vijayakumar Varadarajan, 2022. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis," Future Internet, MDPI, vol. 14(9), pages 1-31, August.

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