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Multi-Objective genetic algorithms for the design of pipe networks.

Tumula, Prasad and Park, N (2004) Multi-Objective genetic algorithms for the design of pipe networks. Journal of Water Resources Planning and Management, 130 (1). pp. 73-84. ISSN 0733-9496

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Abstract/Description

This paper presents a multiobjective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of the network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer’s desire of providing excess head above the minimum allowable head at the nodes and of designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and is applicable to water distribution systems is presented. The present model is applied to two example problems, which are widely reported. Comparison of the present method with other methods revealed that the network resilience based approach gave better results.

Item Type: Article
Print ISSN: 0733-9496
Electronic ISSN: 1943-5452
Uncontrolled Keywords: Hydraulic system; Design; Genetic algorithm; Low cost objective; Reliability objective; Pareto-optimal solutions;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Engineering and the Built Environment
Dewey Decimal Subjects: 600 Technology > 620 Engineering > 627 Hydraulic engineering
000 Computer science, information & general works > 000 Computer science, knowledge & systems > 005 Computer programming, programs & data
Library of Congress Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 1761
Depositing User: RAE Import
Date Deposited: 08 Apr 2008 17:00
Last Modified: 06 Jun 2013 16:42
URI: http://researchrepository.napier.ac.uk/id/eprint/1761

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