Multiobjective Memetic Algorithm Applied to the Optimisation of Water Distribution Systems

Finding low-cost designs of water distribution systems (WDSs) which satisfy appropriate levels of network performance within a manageable time is a complex problem of increasing importance. A novel multi-objective memetic algorithm (MA) is introduced as a solution method to this type of problem. The...

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Veröffentlicht in:Water resources management Jg. 28; H. 8; S. 2229 - 2242
Hauptverfasser: Barlow, Euan, Tanyimboh, Tiku T
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer-Verlag 01.06.2014
Springer Netherlands
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Atmospheric Sciences
Capital expenditures
Civil Engineering
Design optimization
Earth and Environmental Science
Earth Sciences
Earth, ocean, space
Environment
Exact sciences and technology
Genetic algorithms
Geotechnical Engineering & Applied Earth Sciences
Heuristic
Hydraulics
Hydrogeology
Hydrology
Hydrology. Hydrogeology
Hydrology/Water Resources
Learning
Linear programming
Methods
Optimization
Parallel processing
Population
Resource management
Studies
Water distribution
Water distribution systems
Water engineering
Water resources
Water supply engineering
Search space reduction
Parallel computing
Water distribution system design
Penalty-free memetic algorithm
High performance computing
Multi-objective optimisation
surface water
algorithms
cost
water resource management
networks
methodology
ISSN:0920-4741, 1573-1650
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Finding low-cost designs of water distribution systems (WDSs) which satisfy appropriate levels of network performance within a manageable time is a complex problem of increasing importance. A novel multi-objective memetic algorithm (MA) is introduced as a solution method to this type of problem. The MA hybridises a robust genetic algorithm (GA) with a local improvement operator consisting of the classic Hooke and Jeeves direct search method and a cultural learning component. The performance of the MA and the GA on which it is based are compared in the solution of two benchmark WDS problems of increasing size and difficulty. Solutions that are superior to those reported previously in the literature were achieved. The MA is shown to outperform the GA in each case, indicating that this may be a useful tool in the solution of real-world WDS problems. The potential benefits from search space reduction are also demonstrated.
Bibliographie:http://dx.doi.org/10.1007/s11269-014-0608-0
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-014-0608-0