A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems 被引量：2

A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems

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摘要 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 performarice 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. 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.

作者 Xiao-lei DONG Sui-qing LIU Tao TAO Shu-ping LI Kun-lun XIN

机构地区 College of Environmental Science and Technology

出处《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2012年第9期674-686,共13页 浙江大学学报（英文版）A辑（应用物理与工程）

基金 Project (No. 2008AA06A413) supported by the National High-Tech R&D (863) Program of China

关键词 Differential evolution （DE） Genetic algorithms （GAs） OPTIMIZATION Water distribution systems （WDSs） 微分进化(DE ) ；基因算法(气体) ；优化；水分发系统(WDS )

分类号 TU991.3 [建筑科学—市政工程]

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1Alperovits, E., Shamir, U., 1977. Design of water distribution systems. Water Resource Research, 13(6):885-900. [doi: 10.1029/WR0131006P00885].
2Bhave, ER., 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. [doi: 10.1029/92wr00555].
3Bolognesi, A., Bragalli, C., Marchi, A., Artina, S., 2010. Ge- netic heritage evolution by stochastic transmission in the optimal design of water distribution networks. Advances in Engineering Software, 41(5):792-801. [doi:10.1016/j. advengsoft.2009.12.020].
4da Conceigao Cunha, M., Rebeiro, L., 2004. Tabu search algo- rithms for water network optimization: simulated anneal- ing approach. European Journal of Operational Research, 157(3):746-758. [doi:10.1016/S0377-2217(03)00242-X].
5Dandy, G.C., Wilkins, A., Rohrlach, H., 2010. A methodology for Comparing Evolutionary Algorithms for Optimizing Water Distribution Systems. Proceedings of the 12th Wa- ter Distribution System Analysis Symposium, Tucson, USA. American Society of Civil Engineers, Reston, USA, p.786-798. [doi: 10.1061/41203(425)73].
6Deb, K., 2000. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Me- chanics and Engineering, 186(2-4):311-338. [doi:10. 1016/s0045-7825(99)00389-8].
7Eusuff, M.M., Lansey, K.E., 2003. Optimisation of water dis- tribution network design using shuffled frog leaping algo- rithm. Journal of Water Resource Planning and Man- agement, 129(3):210-225. [doi:10.1061/(ASCE)0733- 9496(2003)129:3(210)].
8Fujiwara, 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. [doi: 10.1029/WN026i004p00539].
9Geem, Z.W., 2006. Optimal cost design of water distribution networks using harmony search. Engineering Optimiza- tion, 38(3):259-280. [doi:i 0.1080/03052150500467430].
10Geem, Z.W., 2009. Particle-swarm harmony search for water network design. Engineering Optimization, 41(5):297-311. [doi:l 0.1080/03052150802449227].