Neutral Search Technique for Short-Term Pump Schedule Optimization
Publication: Journal of Water Resources Planning and Management
Volume 136, Issue 1
Abstract
This paper deals with the computation of short-term (typically ) least-cost pump schedule for water distribution systems with on-off type pumps and well-defined operating points. The objective function is to minimize the overall energy charge for a prescribed water consumption while several constraints are considered, such as storage and source limitations, pump setting bounds, nodal mass balances, and the power demand charge. In this study a neutral search technique with genetic algorithms (GAs) is proposed to solve the least-cost pump schedule problem where neutrality is achieved through objective-fitness mapping instead of representational redundancy. We introduce a fitness assignment technique for groups of individuals where grouping is based on objective similarity. As a consequence two different individuals can be equally fit (belonging to the same group) which implies the neutrality. We compare the neutral search technique to two conventional GA approaches on a hypothetical water distribution system.
Get full access to this article
View all available purchase options and get full access to this article.
References
Baltar, A. M., and Fontane, D. G. (2008). “Use of multiobjective particle swarm optimization in water resources management.” J. Water Resour. Plann. Manage., 134(3), 257–265.
Barán, B., Lücken, C., and Sotelo, A. (2005). “Multi-objective pump scheduling optimisation using evolutionary strategies.” Adv. Eng. Software, 36, 39–47.
Bartz-Beielstein, T. (2006). Experimental research in evolutionary computation. Natural computing, Springer, New York.
Coello, C. A., and Pulido, G. T. (2001). “A micro-genetic algorithm for multiobjective optimization.” Lect. Notes Comput. Sci., 1993, 126–140.
Darwin, C. (1859). The origin of species, John Murray, London.
Eiben, A. E., and Smith, J. E. (2003). Introduction to evolutionary computing. Natural computing, 2nd Ed., Springer, Berlin.
Kimura, M. (1983). The neutral theory of molecular evolution, Cambridge University Press, Cambridge.
Mays, L. M. (1999). Water distribution systems handbook, McGraw-Hill, New York.
McCormick, G., and Powell, R. S. (2003). “Optimal pump scheduling in water supply systems with maximum demand charges.” J. Water Resour. Plann. Manage., 129(5), 372–379.
Meuer, H., Strohmaier, E., and Simon, H. (2008). “Top 500 supercomputers sites.” ⟨http://www.top500.org⟩ (June 25, 2008).
Ormsbee, L. E., and Lansey, K. E. (1994). “Optimal control of water supply pumping systems.” J. Water Resour. Plann. Manage., 120(2), 39–47.
Ostfeld, A., and Tubaltzev, A. (2008). “Ant colony optimization of least-cost design and operation of pumping water distribution systems.” J. Water Resour. Plann. Manage., 134(2), 107–118.
Powell, D., and Skolnick, M. M. (1989). “Using genetic algorithms in engineering design optimization with nonlinear constraints.” Proc., Fifth Int. Conf. on Genetic Algorithms, S. Forrest, ed., Morgann Kaufmann, San Mateo, Calif., 421–431.
Rothlauf, F. (2006). Representations for genetic and evolutionary algorithms, 2nd Ed., Springer, Berlin.
Selek, I. (2008). “Istvan Selek’s personal webpage.” ⟨http://www.artificialevolution.net⟩ (July 6, 2008).
Zyl, J. E., Savic, D. A., and Walters, G. A. (2004). “Operational optimization of water distribution systems using hybrid genetic algorithm.” J. Water Resour. Plann. Manage., 130(2), 160–170.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 136 • Issue 1 • January 2010
Pages: 133 - 137
Copyright
© 2010 ASCE.
History
Received: Sep 24, 2007
Accepted: Mar 11, 2009
Published online: Dec 15, 2009
Published in print: Jan 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.