Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO
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Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.
KeywordsMulti-reservoir operating policy Parametric rule Hedging rule Water supply I-NSPSO
This research is supported by the Natural Sciences Foundation of China (71171151, 11201039), Doctoral Fund of Ministry of Education of China (20100141110061) and “PhD Short-time Mobility Program, Wuhan University”. The authors would also like to thank the anonymous reviewers for their review and constructive comments related to this manuscript.
- Bower BT, Hufschmidt MM, Reedy WW (1962) In: Maass A et al (eds) Operating procedures: Their role in the design of water-resource systems by simulation analysis in Design of Water-Resource Systems. Harvard Univ. Press, CambridgeGoogle Scholar
- Clark EJ (1956) Impounding reservoirs. J Am Water Works Assoc 48(4):349–354Google Scholar
- Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterGoogle Scholar
- Fieldsend JE, Singh S (2002) A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence. Proceedings of the 2002 U.K. Workshop on Computational Intelligence, Birmingham, UK. 37–44Google Scholar
- Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. Congress on Evolutionary Computation. Piscataway, New Jersey. Volume 2: 1677-1681Google Scholar
- Hui X, Eberhart RC, Shi Y (2003) Particle swarm with extended memory for multiobjective optimization. IEEE Swarm Intelligence Symposium Proceedings, Indianapolis, Indiana, USA. 193–197Google Scholar
- Kennedy J, Eberhart R (1995) Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks: 1942–1945Google Scholar
- Koutsoyiannis and Economou (2003) Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resour Res 39(6):1170–1186Google Scholar
- Margarita RS, Carlos AC (2006) Multi-objective particle swarm optimizers: a survey of the State-of-the-art. Int J Comput Intell Res 2(3):287–308Google Scholar