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A novel evolutionary meta-heuristic for the multi-objective optimization of real-world water distribution networks

Keedwell, E and Khu, ST (2006) A novel evolutionary meta-heuristic for the multi-objective optimization of real-world water distribution networks Engineering Optimization, 38 (3). pp. 319-336.

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Abstract

Genetic algorithms are currently one of the state-of-the-art meta-heuristic techniques for the optimization of large engineering systems such as the design and rehabilitation of water distribution networks. They are capable of finding near-optimal cost solutions to these problems given certain cost and hydraulic parameters. Recently, multi-objective genetic algorithms have become prevalent in the water industry due to the conflicting nature of these hydraulic and cost objectives. The Pareto-front of solutions can aid decision makers in the water industry as it provides a set of design solutions which can be examined by experienced engineers. However, multi-objective genetic algorithms tend to require a large number of objective function evaluations to arrive at an acceptable Pareto-front, This article investigates a novel hybrid cellular automaton and genetic approach to multi-objective optimization (known as CAMOGA). The proposed method is applied to two large, real-world networks taken from the UK water industry. The results show that the proposed cellular automaton approach can provide a good approximation of the Pareto-front with very few network simulations, and that CAMOGA outperforms the standard multi-objective genetic algorithm in terms of efficiency in discovering similar Pareto-fronts.

Item Type: Article
Authors :
NameEmailORCID
Keedwell, EUNSPECIFIEDUNSPECIFIED
Khu, STs.khu@surrey.ac.ukUNSPECIFIED
Date : 1 April 2006
Identification Number : https://doi.org/10.1080/03052150500476308
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:13
Last Modified : 17 May 2017 11:13
URI: http://epubs.surrey.ac.uk/id/eprint/830485

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