Multipurpose Reservoir Operation Using Particle Swarm Optimization
Publication: Journal of Water Resources Planning and Management
Volume 133, Issue 3
Abstract
This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization (EMPSO) technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. In this study the standard particle swarm optimization (PSO) algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of the problem, a weighted approach is adopted. The results obtained demonstrate that EMPSO is consistently performing better than the standard PSO and genetic algorithm techniques. It is seen that EMPSO is yielding better quality solutions with less number of function evaluations.
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References
Abido, M. A. (2002). “Optimal power flow using particle swarm optimization.” Int. J. Electr. Power Energy Syst., 24(7), 563–571.
Chang, F. H., and Chen, L. (1998). “Real-coded genetic algorithm for rule-based flood control reservoir management.” Water Resour. Manage., 12(3), 185–198.
Clerc, M. (1999). “The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization.” Proc., Congress of Evolutionary Computing, P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, eds., Vol. 3, IEEE, New York, 1951–1957.
Deb, K. (2000). “An efficient constraint handling method for genetic algorithms.” Comput. Methods Appl. Mech. Eng., 186(2–4), 311–338.
Eberhart, R. C., and Kennedy, J. (1995). “A new optimizer using particle swarm theory.” Proc., 6th Symp. on Micro Machine and Human Science, IEEE Service Center, Piscataway, N.J., 39–43.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wiley, Reading, N.Y.
Heidari, M., Chow, V. T., Kokotovic, P. V., and Meredith, D. D. (1971). “Discrete differential dynamic programming approach to water resources systems optimization.” Water Resour. Res., 7(2), 273–282.
Kumar, D. N., and Singh, F. B. (2003). “Folded dynamic programming for optimal operation of multireservoir system.” Water Resour. Manage., 17(5), 337–353.
Larson, R. E. (1968). State increment dynamic programming, Elsevier, New York.
Loucks, D. P., Stedinger, J. R., and Haith, D. A. (1981). Water resource systems planning and analysis, Prentice-Hall, Englewood Cliffs, N.J.
Oliveira, R., and Loucks, D. P. (1997). “Operating rules for multireservoir systems.” Water Resour. Res., 33(4), 839–852.
Parsopoulos, K. E., and Vrahatis, M. N. (2002). “Recent approaches to global optimization problems through particle swarm optimization.” Natural Comput., 1(2–3), 235–306.
Salman, A., Ahmad, I., and Al-Madani, S. (2002). “Particle swarm optimization for task assignment problem.” Microproc. and Microsyst., 26(8), 363–371.
Wardlaw, R., and Sharif, M. (1999). “Evaluation of genetic algorithms for optimal reservoir system operation.” J. Water Resour. Plann. Manage., 125(1), 25–33.
Wurbs, R. A. (1993). “Reservoir-system simulation and optimization models.” J. Water Resour. Plann. Manage., 119(4), 445–472.
Yakowitz, S. (1982). “Dynamic programming applications in water resources.” Water Resour. Res., 18(4), 673–696.
Yeh, W. W.-G. (1985). “Reservoir management and operations models: A state-of-the-art review.” Water Resour. Res., 21(12), 1797–1818.
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Information
Published In
Journal of Water Resources Planning and Management
Volume 133 • Issue 3 • May 2007
Pages: 192 - 201
Copyright
© 2007 ASCE.
History
Received: Jan 19, 2005
Accepted: Apr 3, 2006
Published online: May 1, 2007
Published in print: May 2007
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