Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm
Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. I...
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Veröffentlicht in: | Water (Basel) 2022-04, Vol.14 (8), p.1239 |
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description | Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems. |
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The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w14081239</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; China ; Convergence ; Disasters ; Flood control ; Flood peak ; Floods ; Genetic algorithms ; Heuristic ; Hydroelectric power ; Irrigation ; Linear programming ; Mathematical optimization ; Maximum probable flood ; Objective function ; Optimization ; Optimization algorithms ; Outflow ; Reservoir operation ; Reservoirs ; Simulated annealing ; Water levels</subject><ispartof>Water (Basel), 2022-04, Vol.14 (8), p.1239</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-7f22c636e26ec8c87139ad95609b60781c19206ee48f76813ff280facbb4c5743</citedby><cites>FETCH-LOGICAL-c331t-7f22c636e26ec8c87139ad95609b60781c19206ee48f76813ff280facbb4c5743</cites><orcidid>0000-0002-2619-9842</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Diao, Yanfang</creatorcontrib><creatorcontrib>Ma, Haoran</creatorcontrib><creatorcontrib>Wang, Hao</creatorcontrib><creatorcontrib>Wang, Junnuo</creatorcontrib><creatorcontrib>Li, Shuxian</creatorcontrib><creatorcontrib>Li, Xinyu</creatorcontrib><creatorcontrib>Pan, Jieyu</creatorcontrib><creatorcontrib>Qiu, Qingtai</creatorcontrib><title>Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm</title><title>Water (Basel)</title><description>Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.</description><subject>Algorithms</subject><subject>China</subject><subject>Convergence</subject><subject>Disasters</subject><subject>Flood control</subject><subject>Flood peak</subject><subject>Floods</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Hydroelectric power</subject><subject>Irrigation</subject><subject>Linear programming</subject><subject>Mathematical optimization</subject><subject>Maximum probable flood</subject><subject>Objective function</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Outflow</subject><subject>Reservoir operation</subject><subject>Reservoirs</subject><subject>Simulated annealing</subject><subject>Water levels</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUctKAzEUDaJgqV34BwFXLqbmNXksS7FaKFTUroc0k9SUmcmYjC369U4dEe_mXi7nnPs4AFxjNKVUobsjZkhiQtUZGBEkaMYYw-f_6kswSWmP-mBKyhyNQL1uO1_rCi6qEMpsHpouhgquWxt150MDg4NznYwuLXy2ycZD8DHBTfLNDuoGLus2hoMt4ZOOnTeVhS9HHWv4I-u_Bo1ZtQvRd2_1Fbhwukp28pvHYLO4f50_Zqv1w3I-W2WGUtxlwhFiOOWWcGukkQJTpUuVc6S2HAmJDVYEcWuZdIJLTJ0jEjlttltmcsHoGNwMuv1y7x82dcU-fMSmH1kQnlNEBc1PqOmA2unKFr5xoYva6NOttTehsc73_ZlQmDKRc9wTbgeCiSGlaF3Rxv558bPAqDg5UPw5QL8Bdr93zg</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Diao, Yanfang</creator><creator>Ma, Haoran</creator><creator>Wang, Hao</creator><creator>Wang, Junnuo</creator><creator>Li, Shuxian</creator><creator>Li, Xinyu</creator><creator>Pan, Jieyu</creator><creator>Qiu, Qingtai</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-2619-9842</orcidid></search><sort><creationdate>20220401</creationdate><title>Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm</title><author>Diao, Yanfang ; Ma, Haoran ; Wang, Hao ; Wang, Junnuo ; Li, Shuxian ; Li, Xinyu ; Pan, Jieyu ; Qiu, Qingtai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-7f22c636e26ec8c87139ad95609b60781c19206ee48f76813ff280facbb4c5743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>China</topic><topic>Convergence</topic><topic>Disasters</topic><topic>Flood control</topic><topic>Flood peak</topic><topic>Floods</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Hydroelectric power</topic><topic>Irrigation</topic><topic>Linear programming</topic><topic>Mathematical optimization</topic><topic>Maximum probable flood</topic><topic>Objective function</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Outflow</topic><topic>Reservoir operation</topic><topic>Reservoirs</topic><topic>Simulated annealing</topic><topic>Water levels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Diao, Yanfang</creatorcontrib><creatorcontrib>Ma, Haoran</creatorcontrib><creatorcontrib>Wang, Hao</creatorcontrib><creatorcontrib>Wang, Junnuo</creatorcontrib><creatorcontrib>Li, Shuxian</creatorcontrib><creatorcontrib>Li, Xinyu</creatorcontrib><creatorcontrib>Pan, Jieyu</creatorcontrib><creatorcontrib>Qiu, Qingtai</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Diao, Yanfang</au><au>Ma, Haoran</au><au>Wang, Hao</au><au>Wang, Junnuo</au><au>Li, Shuxian</au><au>Li, Xinyu</au><au>Pan, Jieyu</au><au>Qiu, Qingtai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm</atitle><jtitle>Water (Basel)</jtitle><date>2022-04-01</date><risdate>2022</risdate><volume>14</volume><issue>8</issue><spage>1239</spage><pages>1239-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w14081239</doi><orcidid>https://orcid.org/0000-0002-2619-9842</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms China Convergence Disasters Flood control Flood peak Floods Genetic algorithms Heuristic Hydroelectric power Irrigation Linear programming Mathematical optimization Maximum probable flood Objective function Optimization Optimization algorithms Outflow Reservoir operation Reservoirs Simulated annealing Water levels |
title | Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm |
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