An efficient chaotic water cycle algorithm for optimization tasks

Water cycle algorithm (WCA) is a new population-based meta-heuristic technique. It is originally inspired by idealized hydrological cycle observed in natural environment. The conventional WCA is capable to demonstrate a superior performance compared to other well-established techniques in solving co...

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Bibliographic Details
Published in:Neural computing & applications 2017-01, Vol.28 (1), p.57-85
Main Authors: Heidari, Ali Asghar, Ali Abbaspour, Rahim, Rezaee Jordehi, Ahmad
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Convergence
Data Mining and Knowledge Discovery
Heuristic methods
Hydrologic cycle
Hydrology
Image Processing and Computer Vision
Neural networks
Optimization
Original Article
Performance enhancement
Probability and Statistics in Computer Science
Stochastic processes
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Summary:Water cycle algorithm (WCA) is a new population-based meta-heuristic technique. It is originally inspired by idealized hydrological cycle observed in natural environment. The conventional WCA is capable to demonstrate a superior performance compared to other well-established techniques in solving constrained and also unconstrained problems. Similar to other meta-heuristics, premature convergence to local optima may still be happened in dealing with some specific optimization tasks. Similar to chaos in real water cycle behavior, this article incorporates chaotic patterns into stochastic processes of WCA to improve the performance of conventional algorithm and to mitigate its premature convergence problem. First, different chaotic signal functions along with various chaotic-enhanced WCA strategies (totally 39 meta-heuristics) are implemented, and the best signal is preferred as the most appropriate chaotic technique for modification of WCA. Second, the chaotic algorithm is employed to tackle various benchmark problems published in the specialized literature and also training of neural networks. The comparative statistical results of new technique vividly demonstrate that premature convergence problem is relieved significantly. Chaotic WCA with sinusoidal map and chaotic-enhanced operators not only can exploit high-quality solutions efficiently but can outperform WCA optimizer and other investigated algorithms.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-015-2037-2