CLC number: TU991.3
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2012-08-09
Cited: 14
Clicked: 7338
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.
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