Scheduling of water distribution system rehabilitation using structured messy genetic algorithms

Authors:
Driss Halhal;Godfrey A. Walters;Dragan A. Savic;Driss Ouazar
Affiliations:
Water and Electricity Distribution Co. (RAID) 5 Rue Okuba Ibn Naffy BP 286, Tangier, Morocco;School of Engineering and Computer Science University of Exeter Exeter EX4 4QF, UK G.A.Walters@exeter.ac.uk;School of Engineering and Computer Science University of Exeter Exeter EX4 4QF, UK D.Savic@exeter.ac.uk;Hydraulic Systems Analysis Laboratory Mohammadia School of Engineers (EMI) BP 765, Agdal, Rabat, Morocco ouazar@emi.ac.ma
Venue:
Evolutionary Computation
Year:
1999

Citing 3
Cited 2

Genetic algorithms with sharing for multimodal function optimization

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms

Teaching students to use genetic algorithms to solve optimization problems

Proceedings of the seventh annual consortium for computing in small colleges central plains conference on The journal of computing in small colleges
Multiobjective evolutionary algorithms applied to the rehabilitation of a water distribution system: a comparative study

EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

A methodology is presented for the optimal design and scheduling of investment for the rehabilitation of water distribution networks. Based on the evolutionary programming technique known as Structured Messy Genetic Algorithms, the methodology utilizes a multi-objective formulation which improves the evolutionary process and provides non-dominated optimal solutions over a range of costs and benefits. The model is applied to an example---a small artificial network of fifteen pipes. The effects on the optimal solutions of varying parameters such as interest rate and inflation rate are also investigated.