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Hydrological Cycle Algorithm for Continuous Optimization Problems

Author

Listed:
  • Ahmad Wedyan
  • Jacqueline Whalley
  • Ajit Narayanan

Abstract

A new nature-inspired optimization algorithm called the Hydrological Cycle Algorithm (HCA) is proposed based on the continuous movement of water in nature. In the HCA, a collection of water drops passes through various hydrological water cycle stages, such as flow, evaporation, condensation, and precipitation. Each stage plays an important role in generating solutions and avoiding premature convergence. The HCA shares information by direct and indirect communication among the water drops, which improves solution quality. Similarities and differences between HCA and other water-based algorithms are identified, and the implications of these differences on overall performance are discussed. A new topological representation for problems with a continuous domain is proposed. In proof-of-concept experiments, the HCA is applied on a variety of benchmarked continuous numerical functions. The results were found to be competitive in comparison to a number of other algorithms and validate the effectiveness of HCA. Also demonstrated is the ability of HCA to escape from local optima solutions and converge to global solutions. Thus, HCA provides an alternative approach to tackling various types of multimodal continuous optimization problems as well as an overall framework for water-based particle algorithms in general.

Suggested Citation

  • Ahmad Wedyan & Jacqueline Whalley & Ajit Narayanan, 2017. "Hydrological Cycle Algorithm for Continuous Optimization Problems," Journal of Optimization, Hindawi, vol. 2017, pages 1-25, December.
    • Handle: RePEc:hin:jjopti:3828420
    DOI: 10.1155/2017/3828420
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    References listed on IDEAS

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    1. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    2. Jaco Schutte & Albert Groenwold, 2005. "A Study of Global Optimization Using Particle Swarms," Journal of Global Optimization, Springer, vol. 31(1), pages 93-108, January.
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    Cited by:

    1. Fernando Rubio & Ismael Rodríguez, 2019. "Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems," Complexity, Hindawi, vol. 2019, pages 1-13, April.

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