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Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2008, Vol. 9 Issue (3): 391-400    DOI: 10.1631/jzus.A071448
Environmental & Energy Engineering     
Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II
Xi JIN, Jie ZHANG, Jin-liang GAO, Wen-yan WU
School of Municipal and Environment Engineering, Harbin Institute of Technology, Harbin 150090, China; Faculty of Computing, Engineering and Technology, Staffordshire University, Beaconside, Stafford, UK
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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.

Key wordsWater supply system      Water supply network      Optimal rehabilitation      Multi-objective      Non-dominated sorting Genetic Algorithm (NSGA)     
Received: 23 August 2007      Published: 18 January 2007
CLC:  TU991.33  
Cite this article:

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. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(3): 391-400.

URL:

http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A071448     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2008/V9/I3/391

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