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Sequence analysis-based hyper-heuristics for water distribution network optimisation

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Sequence analysis-based hyper-heuristics for water distribution network optimisation. / Kheiri, Ahmed; Keedwell, Edward; Gibson, Michael J. et al.
In: Procedia Engineering, Vol. 119, No. 1, 2015, p. 1269-1277.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Kheiri, A, Keedwell, E, Gibson, MJ & Savic, D 2015, 'Sequence analysis-based hyper-heuristics for water distribution network optimisation', Procedia Engineering, vol. 119, no. 1, pp. 1269-1277. https://doi.org/10.1016/j.proeng.2015.08.993

APA

Kheiri, A., Keedwell, E., Gibson, M. J., & Savic, D. (2015). Sequence analysis-based hyper-heuristics for water distribution network optimisation. Procedia Engineering, 119(1), 1269-1277. https://doi.org/10.1016/j.proeng.2015.08.993

Vancouver

Kheiri A, Keedwell E, Gibson MJ, Savic D. Sequence analysis-based hyper-heuristics for water distribution network optimisation. Procedia Engineering. 2015;119(1):1269-1277. doi: 10.1016/j.proeng.2015.08.993

Author

Kheiri, Ahmed ; Keedwell, Edward ; Gibson, Michael J. et al. / Sequence analysis-based hyper-heuristics for water distribution network optimisation. In: Procedia Engineering. 2015 ; Vol. 119, No. 1. pp. 1269-1277.

Bibtex

@article{e63be0548aee4016a093b9058d2ead85,
title = "Sequence analysis-based hyper-heuristics for water distribution network optimisation",
abstract = "Hyper-heuristics operate at the level above traditional (meta-)heuristics that 'optimise the optimiser'. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The low level heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic based on sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space.",
keywords = "Hidden markov model, Hyper-heuristic, Water distribution network",
author = "Ahmed Kheiri and Edward Keedwell and Gibson, {Michael J.} and Dragan Savic",
year = "2015",
doi = "10.1016/j.proeng.2015.08.993",
language = "English",
volume = "119",
pages = "1269--1277",
journal = "Procedia Engineering",
issn = "1877-7058",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Sequence analysis-based hyper-heuristics for water distribution network optimisation

AU - Kheiri, Ahmed

AU - Keedwell, Edward

AU - Gibson, Michael J.

AU - Savic, Dragan

PY - 2015

Y1 - 2015

N2 - Hyper-heuristics operate at the level above traditional (meta-)heuristics that 'optimise the optimiser'. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The low level heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic based on sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space.

AB - Hyper-heuristics operate at the level above traditional (meta-)heuristics that 'optimise the optimiser'. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The low level heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic based on sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space.

KW - Hidden markov model

KW - Hyper-heuristic

KW - Water distribution network

U2 - 10.1016/j.proeng.2015.08.993

DO - 10.1016/j.proeng.2015.08.993

M3 - Journal article

AN - SCOPUS:84941142554

VL - 119

SP - 1269

EP - 1277

JO - Procedia Engineering

JF - Procedia Engineering

SN - 1877-7058

IS - 1

ER -