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CLC number: TU991.33

On-line Access: 2007-01-18

Received: 2007-08-23

Revision Accepted: 2007-11-01

Crosschecked: 0000-00-00

Cited: 13

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.3 P.391~400

10.1631/jzus.A071448


Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II


Author(s):  Xi JIN, Jie ZHANG, Jin-liang GAO, Wen-yan WU

Affiliation(s):  School of Municipal and Environment Engineering, Harbin Institute of Technology, Harbin 150090, China; more

Corresponding email(s):   jinxi1978@126.com

Key Words:  Water supply system, Water supply network, Optimal rehabilitation, Multi-objective, Non-dominated sorting Genetic Algorithm (NSGA)


Xi JIN, Jie ZHANG, Jin-liang GAO, Wen-yan WU. Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II[J]. Journal of Zhejiang University Science A, 2008, 9(3): 391~400.

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author="Xi JIN, Jie ZHANG, Jin-liang GAO, Wen-yan WU",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071448"
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%A Xi JIN
%A Jie ZHANG
%A Jin-liang GAO
%A Wen-yan WU
%J Journal of Zhejiang University SCIENCE A
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%P 391~400
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%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071448

TY - JOUR
T1 - Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II
A1 - Xi JIN
A1 - Jie ZHANG
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A1 - Wen-yan WU
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 3
SP - 391
EP - 400
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071448


Abstract: 
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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