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Design optimization of wastewater collection networks by PSO. (English) Zbl 1155.90335
Summary: Optimal design of wastewater collection networks is addressed in this paper by making use of the so-called PSO (Particle Swarm Optimization) technique. This already popular evolutionary technique is adapted for dealing both with continuous and discrete variables as required by this problem. An example of a wastewater collection network is used to show the algorithm performance and the obtained results are compared with those given by using dynamic programming to solve the same problem under the same conditions. PSO is shown to be a promising method to solve optimal design problems regarding, in particular, wastewater collection networks, according to the results herein obtained.
90B10Network models, deterministic (optimization)
90C59Approximation methods and heuristics
[1]Elimam, A. A.; Charalambous, C.: Optimum design of large sewer networks, Journal of environmental engineering 115, No. 6, 1171-1190 (1989)
[2]Rodríguez, J. B. M.: Optimización del diseño de redes y sistemas de alcantarillado, Revista ingeniería hidráulica 4, 119-129 (1983)
[3]A. Botrous, I. El-Hattab, M. Dahab, Design of wastewater collection networks using dynamic programming optimization technique, in: Procs. Of the ASCE Nat. Conf. on Environmental and pipeline Engineering, Kansas City, MO, United States, American Society of Civil Engineers, 2000, pp. 503–512
[4]Desher, D. P.; Davis, P. K.: Designing sanitary sewers with microcomputers, Journal of environmental engineering 112, No. 6, 993-1007 (1986)
[5]Swamee, P. K.: Design of sewer line, Journal of environmental engineering 127, No. 9, 776-781 (2001)
[6]Liang, L. Y.; Thompson, R. G.: Optimising the design of sewer networks using genetic algorithms and tabu search, Engineering, construction and architectural management 11, No. 2, 101-112 (2004)
[7]Afshar, M. H.: Partially constrained ant colony optimization algorithm for the solution of constrained optimization problems: application to storm water network design, Advances in water resources 30, No. 4, 954-965 (2007)
[8]Rossman, L.: Storm water management model user’s manual (version 5.0), (2005)
[9]Kennedy, J.; Eberhart, R. C.: Particle swarm optimization, , 1942-1948 (1995)
[10]Eberhart, R.; Kennedy, J.: A new optimizer using particle swarm theory, , 39-43 (1995)
[11]Jin, Y. X.; Cheng, H. Z.: 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)
[12]Liao, C. J.; Chao-Tang, T.: A discrete version of particle swarm optimization for flowshop scheduling problems, Computers and operations research 34, No. 10, 3099-3111 (2007) · Zbl 1185.90083 · doi:10.1016/j.cor.2005.11.017
[13]Montalvo, I.; Izquierdo, J.; Pérez, R.; Tung, M.: Particle swarm optimization applied to the design of water supply systems, Computers and mathematics with applications 56, No. 3, 769-776 (2008) · Zbl 1155.90486 · doi:10.1016/j.camwa.2008.02.006
[14]X.H. Shi, Y.C. Liang, et al., Particle swarm optimization-based algorithms for TSP and generalized TSP, Information Processing Letters, (2007) (in press). Corrected Proof
[15]Millonas, M. M.; Swarms: Phase transition, and collective intelligence, Artificial life III, 417-445 (1994)
[16]Y.F. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: IEEE International Conference on Evolutionary Computation, 1998, pp. 69–73
[17]Voss, M. S.: Social programming using functional swarm optimization, , 103-109 (2003)
[18]B. Al-kazemi, C.K. Mohan, Multi-phase discrete particle swarm optimization, in: Fourth International Workshop on Frontiers in Evolutionary Algorithms, 2002
[19]Fung, R. Y. K.; Tang, J. F.; Wang, D.: Extension of a hybrid genetic algorithm for nonlinear programming problems with equality and inequality constraints, Computers and operations research 29, 261-274 (2002) · Zbl 1091.90085 · doi:10.1016/S0305-0548(00)00068-X
[20]Y.F. Shi, R.C. Eberhart, Parameter selection in particle swarm optimization, in: Proc. of the 7th Annual Conf. on Evolutionary Programming, 1998, pp. 591–600