WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 1
Abstract
This study applies a recently developed evolutionary algorithm (EA) called state of matter search (SMS) to minimize the total energy deficit in the Karun4 reservoir, Iran. The operation of the Karun4 reservoir is influenced by several factors, which requires a multiple criteria framework for selecting the most suitable solution EA. Five EAs, in addition to the SMS, were evaluated for the reservoir operation problem on the basis of four performance criteria and the fitness function (FF) value. The priority assessment on the basis of FF value revealed that the SMS outperformed the other EAs in converging to the optimal solution. However, judged by the other four performance criteria, based on weighted aggregates sum product assessment (WASPAS) technique, particle swarm optimization (PSO) proved superior to the other EAs. This paper’s results show that the selection of a solution EA for solving complex reservoir optimization problem requires a multicriteria decision-making process. Multiobjective evolutionary algorithms (MOEAs) are well-suited for the task.
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Acknowledgments
The authors are thankful for the insightful and constructive comments submitted by two anonymous reviewers and the associate editor.
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Journal of Water Resources Planning and Management
Volume 143 • Issue 1 • January 2017
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© 2016 American Society of Civil Engineers.
History
Received: Feb 5, 2016
Accepted: Jul 13, 2016
Published online: Sep 21, 2016
Published in print: Jan 1, 2017
Discussion open until: Feb 21, 2017
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