CLC number: TU991.3
On-line Access: 2012-08-30
Received: 2012-03-18
Revision Accepted: 2012-07-13
Crosschecked: 2012-08-09
Cited: 14
Clicked: 7092
Xiao-lei Dong, Sui-qing Liu, Tao Tao, Shu-ping Li, Kun-lun Xin. A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems[J]. Journal of Zhejiang University Science A, 2012, 13(9): 674-686.
@article{title="A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems",
author="Xiao-lei Dong, Sui-qing Liu, Tao Tao, Shu-ping Li, Kun-lun Xin",
journal="Journal of Zhejiang University Science A",
volume="13",
number="9",
pages="674-686",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1200072"
}
%0 Journal Article
%T A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems
%A Xiao-lei Dong
%A Sui-qing Liu
%A Tao Tao
%A Shu-ping Li
%A Kun-lun Xin
%J Journal of Zhejiang University SCIENCE A
%V 13
%N 9
%P 674-686
%@ 1673-565X
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1200072
TY - JOUR
T1 - A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems
A1 - Xiao-lei Dong
A1 - Sui-qing Liu
A1 - Tao Tao
A1 - Shu-ping Li
A1 - Kun-lun Xin
J0 - Journal of Zhejiang University Science A
VL - 13
IS - 9
SP - 674
EP - 686
%@ 1673-565X
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1200072
Abstract: The differential evolution (DE) algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems (WDSs). This paper aims to carry out a comprehensive performance comparison between the new emerged DE algorithm and the most popular algorithm—the genetic algorithm (GA). A total of six benchmark WDS case studies were used with the number of decision variables ranging from 8 to 454. A preliminary sensitivity analysis was performed to select the most effective parameter values for both algorithms to enable the fair comparison. It is observed from the results that the DE algorithm consistently outperforms the GA in terms of both efficiency and the solution quality for each case study. Additionally, the DE algorithm was also compared with the previously published optimization algorithms based on the results for those six case studies, indicating that the DE exhibits comparable performance with other algorithms. It can be concluded that the DE is a newly promising optimization algorithm in the design of WDSs.
[1]Alperovits, E., Shamir, U., 1977. Design of water distribution systems. Water Resource Research, 13(6):885-900.
[2]Bhave, P.R., Sonak, V.V., 1992. A critical study of the linear programming gradient method of optimal design of water supply networks. Water Resource Research, 28(6):1577- 1584.
[3]Bolognesi, A., Bragalli, C., Marchi, A., Artina, S., 2010. Genetic heritage evolution by stochastic transmission in the optimal design of water distribution networks. Advances in Engineering Software, 41(5):792-801.
[4]da Conceição Cunha, M., Rebeiro, L., 2004. Tabu search algorithms for water network optimization: simulated annealing approach. European Journal of Operational Research, 157(3):746-758.
[5]Dandy, G.C., Wilkins, A., Rohrlach, H., 2010. A methodology for Comparing Evolutionary Algorithms for Optimizing Water Distribution Systems. Proceedings of the 12th Water Distribution System Analysis Symposium, Tucson, USA. American Society of Civil Engineers, Reston, USA, p.786-798.
[6]Deb, K., 2000. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4):311-338.
[7]Eusuff, M.M., Lansey, K.E., 2003. Optimisation of water distribution network design using shuffled frog leaping algorithm. Journal of Water Resource Planning and Management, 129(3):210-225.
[8]Fujiwara, O., Khang, D.B., 1990. A two-phase decomposition method for optimal design of looped water distribution networks. Water Resource Research, 26(4):539-549.
[9]Geem, Z.W., 2006. Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38(3):259-280.
[10]Geem, Z.W., 2009. Particle-swarm harmony search for water network design. Engineering Optimization, 41(5):297-311.
[11]Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Company, Boston, MA, USA.
[12]Gupta, I., Gupta, A., Khanna, P., 1999. Genetic algorithm for optimization of water distribution systems. Environmental Modeling & Software, 14(5):437-446.
[13]Kadu, M.S., Gupta, R., Bhave, P.R., 2008. Optimal design of water networks using a modified genetic algorithm. Journal of Water Resource Planning and Management, 134(2):147-160.
[14]Kim, J.H., Kim, T.G., Kim, J.H., Yoon, Y.N., 1994. A study on the pipe network system design using non-linear programming. Journal of Korean Water Resource Association, 27(4):59-67.
[15]Lee, S.C., Lee, S.I., 2001. Genetic algorithms for optimal augmentation of water distribution networks. Journal of Korean Water Resource Association, 34(5):567-575.
[16]Maier, H.R., Simpson, A.R., Zecchin, A.C., Foong, W.F., Phang, K.Y., Seah, H.Y., Tan, C.L., 2003. Ant colony optimization for the design of water distribution systems. Journal of Water Resource Planning and Management, 129(3):200-209.
[17]Mohan, S., Jinesh Babu, K.S., 2010. Optimal water distribution network design with Honey-Bee mating optimization. Journal of Computing in Civil Engineering, 24(1):117- 126.
[18]Prasad, D.T., Park, N.S., 2004. Multiobjective genetic algorithms for design of water distribution networks. Journal of Water Resource Planning and Management, 130(1):73-82.
[19]Reca, J., Martínez, J., 2006. Genetic algorithms for the design of looped irrigation water distribution networks. Water Resource Research, 42:W05416.
[20]Rossman, L.A., 2000. EPANET2-User Manual. National Risk Management Research Laboratory. Office of Research and Development, US Environmental Protection Agency, Cincinnati.
[21]Savic, D.A., Walters, G.A., 1997. Genetic algorithms for least-cost design of water distribution networks. Journal of Water Resource Planning and Management, 123(2):67-77.
[22]Schaake, J., Lai, D., 1969. Linear Programming and Dynamic Programming Applications to Water Distribution Network Design. Research Report No. 116, Hydrodynamics Laboratory, Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
[23]Simpson, A.R., Dandy, G.C., Murphy, L.J., 1994. Genetic algorithms compared to other techniques for pipe optimization. Journal of Water Resource Planning and Management, 120(4):423-443.
[24]Storn, R., Price, K., 1995. Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Space. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA.
[25]Storn, R., Price, K.V., 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341-359.
[26]Suribabu, C.R., Neelakantan, T.R., 2006. Design of water distribution networks using particle swarm optimization. Journal of Urban Water, 3(2):111-120.
[27]Tolson, B.A., Asadzadeh, M., Maier, H.R., Zecchin, A.C., 2009. Hybrid discrete dynamically dimensioned search (HD-DDS) algorithm for water distribution system design optimization. Water Resources Research, 45:W12416.
[28]Varma, K.V.K., Narasimhan, S., Bhallamudi, S.M., 1997. Optimal design of water distribution systems using an NLP method. Journal of Environmental Engineering, 123(4):381-388.
[29]Zheng, F., Simpson, A.R., Zecchin, A.C., 2010. A Method of Assessing the Performance of Genetic Algorithm Optimization Water Distribution Design. Proceedings of the 12th Water Distribution System Analysis Symposium, Tucson, USA. American Society of Civil Engineers, Reston, USA, p.771-785.
[30]Zheng, F., Simpson, A.R., Zecchin, A.C., 2011. A combined NLP-differential evolution algorithm approach for the optimization of looped water distribution systems. Water Resources Research, 47:W08531.
[31]Zheng, F., Zecchin, A.C., Simpson, A.R., 2012. A self-adaptive differential evolution algorithm applied to water distribution system optimization. Journal of Computing in Civil Engineering ASCE, in press.
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