Final published version, 373 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -