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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Vydáno v:	Water resources management Ročník 28; číslo 8; s. 2229 - 2242
Hlavní autoři:	Barlow, Euan, Tanyimboh, Tiku T
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer-Verlag 01.06.2014 Springer Netherlands Springer Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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. 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.[PUBLICATION ABSTRACT]
Author	Barlow, Euan Tanyimboh, Tiku T
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Cites_doi	10.1061/(ASCE)0733-9496(2003)129:3(210) 10.1007/s11269-011-9775-4 10.1016/j.asoc.2009.07.010 10.1016/j.envsoft.2008.06.008 10.1007/s11269-012-0158-2 10.1680/wlcfwdnm.31661 10.1007/s11269-009-9434-1 10.1080/0305215X.2010.497185 10.1007/s10589-007-9121-1 10.1061/(ASCE)0733-9496(2008)134:2(147) 10.1080/03052150801981915 10.1016/j.advengsoft.2009.12.020 10.1109/4235.996017 10.1017/CBO9780511921247 10.1007/978-1-4615-5563-6 10.1080/10286600701838626 10.1029/2008WR007673 10.2166/hydro.2004.0012 10.1029/2005WR004383 10.1029/2011WR010394
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Keywords	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
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Snippet	Finding low-cost designs of water distribution systems (WDSs) which satisfy appropriate levels of network performance within a manageable time is a complex...
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SubjectTerms	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
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Title	Multiobjective Memetic Algorithm Applied to the Optimisation of Water Distribution Systems
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