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The Enhanced Honey-Bee Mating Optimization Algorithm for Water Resources Optimization

Author

Listed:
  • Mohammad Solgi

    (University of Tehran)

  • Omid Bozorg-Haddad

    (University of Tehran)

  • Hugo A. Loáiciga

    (University of California)

Abstract

Evolutionary and meta-heuristic algorithms are widely used to solve water resources optimization problems. In this context, the honey bee mating optimization (HBMO) algorithm, inspired by the mating ritual of honey bees, is a reliable and efficient algorithm. The HBMO algorithm is modified in this work leading to the Enhanced HBMO (EHBMO) algorithm. The EHBMO is then applied to solve several unconstrained/constrained mathematical benchmark functions and a multi-reservoir problem. The performance of the EHBMO is compared with those of the elitist genetic algorithm (EGA) and the HBMO algorithm. The results show that the EHBMO achieves a better solution in a smaller number of functional evaluations and with less variance of results about global optima in comparison with the EGA and the HBMO algorithm.

Suggested Citation

  • Mohammad Solgi & Omid Bozorg-Haddad & Hugo A. Loáiciga, 2017. "The Enhanced Honey-Bee Mating Optimization Algorithm for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 885-901, February.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:3:d:10.1007_s11269-016-1553-x
    DOI: 10.1007/s11269-016-1553-x
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    References listed on IDEAS

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    1. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
    2. E. Fallah-Mehdipour & O. Bozorg Haddad & M. Mariño, 2012. "Real-Time Operation of Reservoir System by Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4091-4103, November.
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    Cited by:

    1. Majid Mohammadi & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2019. "Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4767-4782, November.
    2. Shahmir Janjua & Ishtiaq Hassan & Muhammad Umair Ali & Malik Muhammad Ibrahim & Amad Zafar & Sangil Kim, 2021. "Addressing Social Inequality and Improper Water Distribution in Cities: A Case Study of Karachi, Pakistan," Land, MDPI, vol. 10(11), pages 1-15, November.

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