Multi-Objective Optimization of Water-Sedimentation-Power in Reservoir Based on Pareto-Optimal Solution
A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimi...
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| Published in | Transactions of Tianjin University Vol. 14; no. 4; pp. 282 - 288 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Tianjin University
01.08.2008
School of Civil Engineering, Tianjin University, Tianjin 300072, China |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-4982 1995-8196 |
| DOI | 10.1007/s12209-008-0048-0 |
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| Abstract | A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization （IMOPSO） is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions （A, B, C） of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ）, IMOPSO has close global optimization capability and is suitable for multi-objective optimization problems. |
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| AbstractList | TV6; A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover,the inertia weight serf-adjusting mechanism and Pareto-optimal archive are introduced into the par-ticle swarm optimization and an improved multi-objective particle swarm optimization (IMOPSO) is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions (A, B, C) of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sedi-ment deposition, sediment deposition and power generation, and power generation, respectively.The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Com-pared with the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ), IMOPSO has close global op-timization capability and is suitable for multi-objective optimization problems. A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization (IMOPSO) is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions (A, B, C) of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-II (NSGA-II), IMOPSO has close global optimization capability and is suitable for multi-objective optimization problems. A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation, sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization （IMOPSO） is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions （A, B, C） of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition, sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ）, IMOPSO has close global optimization capability and is suitable for multi-objective optimization problems. |
| Author | 李辉 练继建 |
| AuthorAffiliation | School of Civil Engineering, Tianjin University, Tianjin 300072, China |
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| Cites_doi | 10.1109/4235.996017 10.1109/TAC.1976.1101338 10.1162/evco.1994.2.3.221 10.1007/978-3-642-17144-4_1 10.1109/ICNN.1995.488968 |
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| Copyright | Tianjin University and Springer-Verlag GmbH 2008 Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | multi-objective optimization of water-sedimentation-power optimal operation of reservoir particle swarm optimization Pareto-optimal solution optimal operation of reser-voir |
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| References | Lin (CR8) 1976; 21 Zitzler, Laumanns, Bleuler (CR10) 2004; 2 CR11 Deb, Pratap, Agarwal (CR12) 2002; 6 Bao, Huang, Yang (CR2) 2005; 21 Horn, Nafpliotis (CR6) 1993 Shao, Xia, Sun (CR1) 1998; 16 CR7 CR9 Fonseca, Fleming (CR4) 1993 Lin, Xu, Pan (CR3) 1992; 3 Srinivas, Deb (CR5) 1994; 2 D. Shao (48_CR1) 1998; 16 N. Srinivas (48_CR5) 1994; 2 48_CR7 E. Zitzler (48_CR10) 2004; 2 J. Horn (48_CR6) 1993 48_CR9 J. Lin (48_CR8) 1976; 21 W. Bao (48_CR2) 2005; 21 C. M. Fonseca (48_CR4) 1993 X. Lin (48_CR3) 1992; 3 48_CR11 K. Deb (48_CR12) 2002; 6 |
| References_xml | – ident: CR9 – volume: 6 start-page: 182 issue: 2 year: 2002 end-page: 197 ident: CR12 article-title: A fast and elitist multi-objective genetic algorithm: NSGA-II [J] publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – volume: 16 start-page: 7 issue: 4 year: 1998 end-page: 11 ident: CR1 article-title: Study on real optimal operation model for the reservoir of water resources multi-objective comprehensive utilization [J] publication-title: International Journal Hydroelectric Energy – volume: 21 start-page: 641 issue: 5 year: 1976 end-page: 650 ident: CR8 article-title: Multi-objective problems: Pareto-optimal solutions by method of proper equality constraints [J] publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.1976.1101338 – ident: CR7 – volume: 2 start-page: 3 issue: 1 year: 2004 end-page: 37 ident: CR10 article-title: A tutorial on evolutionary multi-objective optimization [J] publication-title: Metaheuristics for Multiobjective Optimization – volume: 21 start-page: 421 issue: 4 year: 2005 end-page: 424 ident: CR2 article-title: Research on reservoir optimal operation based on multi-purpose dynamic model [J] publication-title: Journal of Xi’an University of Technology – volume: 3 start-page: 112 issue: 2 year: 1992 end-page: 119 ident: CR3 article-title: Multi-objective optimal operation for comprehensive utilization of reservoirs [J] publication-title: Advances in Water Science – start-page: 141 year: 1993 end-page: 153 ident: CR4 article-title: Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization [C] publication-title: Proceedings of the Fifth International Conference on Genetic Algorithms – volume: 2 start-page: 221 issue: 3 year: 1994 end-page: 248 ident: CR5 article-title: Multi-objective optimization using nondominated sorting in genetic algorithms [J] publication-title: IEEE Transactions on Evolutionary Computation – year: 1993 ident: CR6 publication-title: Multi-objective optimization using the niched Pareto genetic algorithm [R] – ident: CR11 – volume: 16 start-page: 7 issue: 4 year: 1998 ident: 48_CR1 publication-title: International Journal Hydroelectric Energy – volume: 21 start-page: 421 issue: 4 year: 2005 ident: 48_CR2 publication-title: Journal of Xi’an University of Technology – volume: 2 start-page: 221 issue: 3 year: 1994 ident: 48_CR5 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1162/evco.1994.2.3.221 – start-page: 141 volume-title: Proceedings of the Fifth International Conference on Genetic Algorithms year: 1993 ident: 48_CR4 – volume: 2 start-page: 3 issue: 1 year: 2004 ident: 48_CR10 publication-title: Metaheuristics for Multiobjective Optimization doi: 10.1007/978-3-642-17144-4_1 – volume: 3 start-page: 112 issue: 2 year: 1992 ident: 48_CR3 publication-title: Advances in Water Science – ident: 48_CR11 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 48_CR12 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – ident: 48_CR7 doi: 10.1109/ICNN.1995.488968 – volume: 21 start-page: 641 issue: 5 year: 1976 ident: 48_CR8 publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.1976.1101338 – ident: 48_CR9 – volume-title: Multi-objective optimization using the niched Pareto genetic algorithm [R] year: 1993 ident: 48_CR6 |
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| Title | Multi-Objective Optimization of Water-Sedimentation-Power in Reservoir Based on Pareto-Optimal Solution |
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