Journal
APPLIED MATHEMATICAL MODELLING
Volume 38, Issue 17-18, Pages 4480-4492Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2014.02.030
Keywords
Reservoir flood control; Evolutionary computation; Particle swarm optimization; Chaotic map; Death penalty function
Funding
- China Postdoctoral Science Special Foundation [201104323]
- Anhui Provincial Natural Science Foundation [1408085QG137]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130111120015]
- China Postdoctoral Science Foundation [20100480679]
- National Natural Science Foundation [71131002, 71071045]
- Author of National Excellent Doctoral Dissertation of PR China [200982]
- Fundamental Research Funds for the Central Universities [2012-IV-102, 2011HGRJ0006, 2012HGBZ0189]
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Reservoir flood control operation is a complex engineering optimization problem with a large number of constraints. In order to solve this problem, a chaotic particle swarm optimization (CPSO) algorithm based on the improved logistic map is presented, which uses the discharge flow process as the decision variables combined with the death penalty function. According to the principle of maximum eliminating flood peak, a novel flood control operation model has been established with the goal of minimum standard deviation of the discharge flow process. At the same time, a piecewise linear interpolation function (PLIF) is applied to deal with the constraints for solving objective function. The performance of the proposed model and method is evaluated on two typical floods of Three Gorges reservoir. In comparison with existing models and other algorithms, the proposed model and algorithm can generate better solutions with the minimal flood peak discharge and the maximal peak-clipping rate for reservoir flood control operation. (C) 2014 Elsevier Inc. All rights reserved.
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