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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.
Water Cycle Algorithm (WCA) as a population-based optimization algorithm that tries to update the swarm concerning the top solutions (sea and rivers), and it has been utilized in many applications such as truss structures, unconstrained, constrained engineering design problems, and multi-objective (unconstrained, constrained) optimization problems.
The WCA is an easy and straightforward optimizer, while it is a mediocre class method in terms of performance. The primary method is not published in top prestigious computer science journals. However, in 2012, it was one of the new methods for engineering problems.
The WCA cannot beat LSHADE, SADE, CODE, and other top DE variants, and other advanced PSO variants on most function optimization problems. The original paper suffers from a verification bias, and it never validated using IEEE CEC benchmarks such as IEEE CEC2011 and IEEE CEC 2010. No advanced optimizer on CEC has been considered in the original work to validate WCA. For the composition problems of IEEE CEC 2017, it faces serious stagnation problems and low convergence speed. It has a high rejection rate for top journals, and it is not the best choice as the base method of leading computer science research. This method has no chance for IEEE transactions journals to be applied as a core method.
How to cite?
Heidari, Ali Asghar, Rahim Ali Abbaspour, and Ahmad Rezaee Jordehi. "An efficient chaotic water cycle algorithm for optimization tasks." Neural Computing and Applications 28, no. 1 (2017): 57-85.
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