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Journal of Optimization
Volume 2017, Article ID 3828420, 25 pages
Research Article

Hydrological Cycle Algorithm for Continuous Optimization Problems

School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

Correspondence should be addressed to Ahmad Wedyan;

Received 2 August 2017; Revised 13 November 2017; Accepted 22 November 2017; Published 17 December 2017

Academic Editor: Efren Mezura-Montes

Copyright © 2017 Ahmad Wedyan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.