Adaptive locally constrained genetic algorithm for least-cost water distribution network design

This paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world nonlinear optimisat...

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Bibliographic Details
Published in:Journal of hydroinformatics 2014-01, Vol.16 (2), p.288-301
Main Authors: JOHNS, Matthew B, KEEDWELL, Edward, SAVIC, Dragan
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Applied sciences
Buildings. Public works
Chromosomes
Computation methods. Tables. Charts
Design optimization
Distribution
Distribution management
Distribution. Storage
Exact sciences and technology
Genetic algorithms
Heuristic methods
Mathematical models
Mutation
Network design
Piezometric head
Solution space
Solutions
Structural analysis. Stresses
Systems design
Water distribution
Water engineering
Water supply. Pipings. Water treatment
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Description
Summary:This paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world nonlinear optimisation problems, including water system design and maintenance problems. In this work we propose a heuristic-based approach to the mutation of chromosomes with the algorithm employing an adaptive mutation operator which utilises hydraulic head information and an elementary heuristic to increase the efficiency of the algorithm's search into the feasible solution space. In almost all test instances ALCO-GA displays faster convergence and reaches the feasible solution space faster than the standard genetic algorithm. ALCO-GA also achieves high optimality when compared to solutions from the literature and often obtains better solutions than the standard genetic algorithm.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2013.218