Self-Adaptive PSO-GA Hybrid Model for Combinatorial Water Distribution Network Design
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 4, Issue 1
Abstract
In modern civilization, water distribution network has a substantial role in preserving the desired living standard. It has different components such as pipe, pump, and control valve to convey water from the supply source to the consumer withdrawal points. Among these elements, optimal sizing of pipes has great importance because more than 70% of the project cost is incurred on it. Unfortunately, optimal pipe sizing falls in the category of nonlinear polynomial time hard (NP-hard) problems. Hence, solid research activities march on because of two facts, namely, importance and complexity of the problem. The literature revealed that the stochastic optimization algorithms are successful in exploring the combination of least-cost pipe diameters from the commercially available discrete diameter set, but with the expense of significant computational effort. The hybrid model PSO-GA, presented in this paper aimed to effectively utilize local and global search capabilities of particle swarm optimization (PSO) and genetic algorithm (GA), respectively, to reduce the computational burden. The analyses on different water distribution networks uncover that the proposed hybrid model is capable of exploring the optimal combination of pipe diameters with minimal computational effort.
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References
Alperovits, E., and Shamir, U. (1977). “Design of optimal water distribution systems.” Water Resour. Res., 13(6), 885–900.
Bhave, P. R. (1978). “Noncomputer optimization of single-source networks.” J. Environ. Eng. Div., 104(4), 799–814.
Dandy, G. C., and Engelhardt, M. O. (2006). “Multi-objective trade-offs between cost and reliability in the replacement of water mains.” J. Water Resour. Plann. Manage., 132(2), 79–88.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, New York.
Eberhart, R. C., and Shi, Y. (2001). “Tracking and optimizing dynamic systems with particle swarms.” Proc., IEEE Int. Congress on Evolutionary Computation, IEEE, New York, 94–100.
Fujiwara, O., and Khang, D. B. (1990). “A two-phase decomposition method for optimal design of looped water distribution networks.” Water Resour. Res., 26(4), 539–549.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, MA.
Goldberg, D. E., and Kuo, C. H. (1987). “Genetic algorithm in pipeline optimization.” J. Comput. Civ. Eng., 1(2), 128–141.
Holland, J. H. (1975). Adaptation in natural and artificial systems, Univ. of Michigan Press, Ann Arbor, MI.
Kadu, M. S., Gupta, R., and Bhave, P. R. (2008). “Optimal design of water networks using a modified genetic algorithm.” J. Water Resour. Plann. Manage., 134(2), 147–160.
Keedwell, E., and Khu, S. (2006). “Novel cellular automata approach to optimal water distribution network design.” J. Comput. Civ. Eng., 20(1), 49–56.
Kennedy, J., and Eberhart, R. C. (1995). “Particle swarm optimization.” Proc., IEEE Int. Conf. on Neural Networks, IEEE, New York, 1942–1948.
Modak, P. M., and Dhoondia, J. (1991). “A computer program in quick BASIC for the heuristic design of looped water distribution networks: User instructions for LOOP version 4.0.” Asia Water Supply and Sanitation Sector Development Project, RAS/86/160, United Nations Development Programme and World Bank, The World Bank, Washington.
Mohan, S., and Babu, J. K. S. (2009). “Optimal water distribution network design using heuristics based algorithm.” J. Comput. Civ. Eng., 23(5), 249–257.
Mohan, S., and Babu, J. K. S. (2010). “Optimal water distribution network design with honey-bee mating optimization.” J. Comput. Civ. Eng., 24(1), 117–126.
Montalvo, I., Izquierdo, J., Perez, R., and Tung, M. M. (2008). “Particle swarm optimization model to the design of water supply systems.” Comput. Math. App., 56(3), 769–776.
Premalatha, K., and Natarajan, A. M. (2010). “Combined heuristic optimization for global maximization.” Int. J. Adv. Soft Comput. App., 2(1), 85–99.
Rossman, L. A. (2000). EPANET user’s manual, Risk Reduction Engineering Laboratory, U.S. Environmental Protection Agency, Cincinnati.
Savic, D. A., and Walters, G. A. (1997). “Genetic algorithms for least-cost design of water distribution networks.” J. Water Resour. Plann. Manage., 123(2), 67–77.
Shi, Y., and Eberhart, R. C. (1998). “A modified particle swarm optimizer.” Proc., IEEE Int. Conf. on Evolutionary Computation, IEEE, New York, 69–73.
Simpson, A. R., Dandy, G. C., and Murphy, L. J. (1994). “Genetic algorithms compared to other techniques for pipe optimization.” J. Water Resour. Plann. Manage., 120(4), 423–443.
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Information
Published In
Journal of Pipeline Systems Engineering and Practice
Volume 4 • Issue 1 • February 2013
Pages: 57 - 67
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Apr 23, 2011
Accepted: May 16, 2012
Published online: May 21, 2012
Published in print: Feb 1, 2013
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