Sequence Analysis-based Hyper-heuristics for Water Distribution Network Optimisation

被引:10
|
作者
Kheiri, Ahmed [1 ]
Keedwell, Edward [1 ]
Gibson, Michael J. [1 ]
Savic, Dragan [1 ]
机构
[1] Univ Exeter, Coll Engn Math & Phys Sci, North Pk Rd, Exeter EX4 4QF, Devon, England
基金
英国工程与自然科学研究理事会;
关键词
Hyper-heuristic; Water Distribution Network; Hidden Markov Model; DESIGN; SYSTEMS;
D O I
10.1016/j.proeng.2015.08.993
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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 hyperheuristic 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. (C) 2015 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:1269 / 1277
页数:9
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