Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply

Research Article| November 15 2017 Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply Mohammad Ehteram; Mohammad Ehteram 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran Search for other works by this author on: This Site PubMed Google Scholar Hojat Karami; Hojat Karami 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran E-mail: hkarami@semnan.ac.ir Search for other works by this author on: This Site PubMed Google Scholar Sayed-Farhad Mousavi; Sayed-Farhad Mousavi 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran Search for other works by this author on: This Site PubMed Google Scholar Saeed Farzin; Saeed Farzin 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran Search for other works by this author on: This Site PubMed Google Scholar Ozgur Kisi Ozgur Kisi 2School of Natural Sciences and Engineering, Ilia State University, Tiblisi, Georgia Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2018) 67 (1): 54–67. https://doi.org/10.2166/aqua.2017.109 Article history Received: June 12 2017 Accepted: September 27 2017 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Mohammad Ehteram, Hojat Karami, Sayed-Farhad Mousavi, Saeed Farzin, Ozgur Kisi; Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply. Journal of Water Supply: Research and Technology-Aqua 1 February 2018; 67 (1): 54–67. doi: https://doi.org/10.2166/aqua.2017.109 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Abstract This study evaluates three contemporary evolutionary algorithms, namely, shark, genetic, and particle swarm algorithms, for optimization in reservoir operation and water supply. The Klang gate dam in Malaysia is selected as the case study to optimize reservoir operation. The key objective of this study is the minimization of water deficits based on demands and released water. The global solution of the problem is computed based on software Lingo and the average solution of the shark algorithm is able to attain 99% of global solution. As well, the shark algorithm can furnish demand values at a faster convergence rate than both genetic and particle swarm algorithms. The reliability index and resiliency index, as useful indices in water resource management, are used and the values of these indices have the highest percent for the shark algorithm, indicating its superiority over other evolutionary algorithms. genetic algorithm, particle swarm algorithm, shark algorithm, water resource management © IWA Publishing 2018 You do not currently have access to this content.