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Particle swarm optimization applied to the design of water supply systems. (English) Zbl 1155.90486

Summary: In the past decade, evolutionary methods have been used by various researchers to tackle optimal design problems for water supply systems (WSS). Particle Swarm Optimization (PSO) is one of these evolutionary algorithms which, in spite of the fact that it has primarily been developed for the solution of optimization problems with continuous variables, has been successfully adapted in other contexts to problems with discrete variables.

In this work we have applied one of the variants of this algorithm to two case studies: the Hanoi water distribution network and the New York City water supply tunnel system. Both cases occur frequently in the related literature and provide two standard networks for benchmarking studies. This allows us to present a detailed comparison of our new results with those previously obtained by other authors.

90C59Approximation methods and heuristics
90B10Network models, deterministic (optimization)
90C90Applications of mathematical programming
[1]Goulter, I. C.; Bouchart, F.: Quantitative approaches to reliability assessment in pipe networks, Journal of transportation engineering 112, No. 3, 287-301 (1986)
[2]Goulter, I. C.; Bouchart, F.: Reliability-constrained pipe network model, Journal of hydraulic engineering 116, No. 2, 211-229 (1990)
[3]D.A. Savic, G.A. Walters, Genetic operators and constraint handling for pipe network optimization, in: Evolutionary Computing, AISB Workshop, 1995, pp. 154–165
[4]Wu, Z. Y.; Simpson, A. R.: Competent genetic-evolutionary optimization of water distribution systems, Journal of computing in civil engineering 15, No. 2, 89-101 (2001)
[5]A.S. Matías, Diseño de redes de distribución de agua contemplando la fiabilidad, mediante algoritmos genéticos, Ph.D. Thesis, Universidad Politécnica de Valencia, Valencia, Spain. 2003
[6]Wu, Z. Y.; Walski, T.: Self-adaptive penalty cost for optimal design of water distribution systems, Journal of water resources planning and management 131, No. 3, 181-192 (2005)
[7]Maier, H. R.; Simpson, A. R.; Zecchin, A. C.; Foong, W. K.; Phang, K. Y.; Seah, H. Y.; Tan, C. L.: Ant colony optimization for design of water distribution systems, Journal of water resources planning and management 129, No. 3, 200-209 (2003)
[8]Zecchin, A. C.; Simpson, A. R.; Maier, H. R.; Nixon, J. B.: Parametric study for an ant algorithm applied to water distribution system optimization, IEEE transactions on evolutionary computation 9, No. 2, 175-191 (2005)
[9]Cunha, M. D. C.; Sousa, J.: Water distribution network design optimization: simulated annealing approach, Journal of water resources planning and management 125, No. 4, 214-221 (1999)
[10]Liong, S. -Y.; Atiquzzaman, M.: Optimal design of water distribution network using shuffled complex evolution, Journal of the institution of engineers, Singapore 44, No. 1, 93-107 (2004)
[11]Geem, Z. W.: Optimal cost design of water distribution networks using harmony search, Engineering optimization 38, No. 3, 259-280 (2006)
[12]A. Colorni, M. Dorigo, F. Maffioli, V. Maniezzo, G. Righini, M. Trubian, Heuristics from nature for hard combinatorial optimization problems, 1996 · Zbl 0863.90120 · doi:10.1111/j.1475-3995.1996.tb00032.x
[13]Dong, Y.; Tang, B. X. J.; Wang, D.: An application of swarm optimization to nonlinear programming, Computers mathematics with applications 49, No. 11–12, 1655-1668 (2005) · Zbl 1127.90407 · doi:10.1016/j.camwa.2005.02.006
[14]Kennedy, J.; Eberhart, R. C.: Particle swarm optimization, (1995)
[15]Izquierdo, J.; Pérez, R.; Iglesias, P. L.: Mathematical models and methods in the water industry, Mathematical and computer modelling 39, No. 11–12, 1353-1374 (2004) · Zbl 1093.91051 · doi:10.1016/j.mcm.2004.06.012
[16]Dandy, G. C.; Engelhardt, M. O.: Multi-objective trade-offs between cost and reliability in the replacement of water mains, Journal of water resources planning and management 132, No. 2, 79-88 (2006)
[17]Martínez, J. B.: Quantifying the economy of water supply looped networks, Journal of hydraulic engineering 133, No. 1, 88-97 (2007)
[18]Y. Shi, R. Eberhart, A modified particle swarm optimizer, in: Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, 4–9 May 1998, 1998, pp. 69–73
[19]M.S. Voss, Social programming using functional swarm optimization, in: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS03), Purdue University, Indianapolis, Indiana, 2003
[20]Shi, X. H.; Liang, Y. C.; Lee, H. P.; Lu, C.; Wang, Q. X.: Particle swarm optimization-based algorithms for tsp and generalized tsp, Information processing letters 103, No. 5, 169-176 (2007) · Zbl 1187.90238 · doi:10.1016/j.ipl.2007.03.010
[21]B. Al-Kazemi, C.K. Mohan, Multi-phase discrete particle swarm optimization, in: JCIS, 2002, pp. 622–625
[22]R. Rastegar, M.R. Meybodi, K. Badie, A new discrete binary particle swarm optimization based on learning automata, in: 2004 International Conference on Machine Learning and Applications, 2004
[23]Liao, C. -J.; Tseng, C. -T.; Luarn, P.: A discrete version of particle swarm optimization for flowshop scheduling problems, Computers and operation research 34, No. 10, 3099-3111 (2007) · Zbl 1185.90083 · doi:10.1016/j.cor.2005.11.017
[24]Jina, Y. -X.; Chenga, H. -Z.; Yanb, J. -Y.; Zhang, L.: New discrete method for particle swarm optimization and its application in transmission network expansion planning, Electric power systems research 77, No. 3–4, 227-233 (2007)
[25]A. Zecchin, H. Maier, A. Simpson, A. Roberts, M. Berrisford, M. Leonard, Max-min ant system applied to water distribution system optimisation, in: Proceedings MODSIM 2003: International Congress on Modelling and Simulation, Townsville, Queensland, Australia, 2003
[26]Dandy, G. C.; Simpson, A. R.; Murphy, L. J.: An improved genetic algorithm for pipe network optimization, Water resources research 32, No. 2, 449-458 (1996)
[27]Izquierdo, J.; Montalvo, I.; Pérez, R.; Fuertes, V. S.: Design optimization of wastewater collection networks by PSO, Computers and mathematics with applications 56, No. 3, 777-784 (2008) · Zbl 1155.90335 · doi:10.1016/j.camwa.2008.02.007