Hybrid Differential Evolution and Krill Herd Algorithm for the Optimal Design of Water Distribution Networks
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 1
Abstract
For optimally designing water distribution networks (WDNs), the nondeterministic polynomial hard problem, a novel hybrid model, is introduced with the combined features of evolutionary and swarm intelligence techniques. An evolutionary algorithm with better exploration properties, differential evolution (DE), and the swarm intelligence technique with better exploitation properties, namely the krill herd algorithm (KHA), is considered for this purpose. Because exploration and exploitation are the essential features of the metaheuristic algorithms, the hybrid algorithm with a combination of the DE and KHA features, the DE-KHA, resulted in a balanced search methodology. The results on the application of the proposed model on well-studied benchmark problems have demonstrated its enhanced search behavior, converging faster to the promising results with considerable robustness. Moreover, compared with other competing algorithms reported for optimally designing the WDNs, the DE-KHA outperforms with better computational efficiency. Additionally, considering the few control parameters that have to be calibrated for their optimal values, the computational burden will be less for performing the sensitivity analysis. As a result, considering the solution precision, quick convergence ability, and robustness of DE-KHA, the study suggests the algorithm for efficiently handling real-life case studies.
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Data Availability Statement
The data for the benchmark problems considered in the present study are taken from the Centre for Water Systems, Benchmark Problems, University of Exeter (emps.exeter.ac.uk/engineering/research/cws/resources/benchmarks/pareto/) and could also be found in Geem (2006).
All the models or codes that support the findings of this study are available from the corresponding author (written in MATLAB software and compiled with the simulation software EPANET using the MATLAB-EPANET toolkit).
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 1 • January 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 15, 2021
Accepted: Aug 26, 2021
Published online: Oct 8, 2021
Published in print: Jan 1, 2022
Discussion open until: Mar 8, 2022
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