A sequence-based selection hyper-heuristic with online learning is used to optimise 12 water distribution networks of varying sizes. The hyper-heuristic results are compared with those produced by five multiobjective evolutionary algorithms. The comparison demonstrates that the hyper-heuristic is a computationally efficient alternative to a multiobjective evolutionary algorithm. An offline learning algorithm is used to enhance the optimisation performance of the hyper-heuristic. The optimisation results of the offline trained hyper-heuristic are analysed statistically, and a new offline learning methodology is proposed. The new methodology is evaluated, and shown to produce an improvement in performance on each of the 12 networks. Finally, it is demonstrated that offline learning can be usefully transferred from small, computationally inexpensive problems, to larger computationally expensive ones, and that the improvement in optimisation performance is statistically significant, with 99% confidence.

中文翻译：

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具有选择超启发式的离线学习：在配水网络优化中的应用

基于序列的选择超启发式和在线学习用于优化 12 个不同规模的配水网络。将超启发式结果与五种多目标进化算法产生的结果进行比较。比较表明，超启发式算法是多目标进化算法的计算高效替代方案。离线学习算法用于增强超启发式算法的优化性能。对离线训练的超启发式算法的优化结果进行统计分析，提出一种新的离线学习方法。对新方法进行了评估，并证明其在 12 个网络中的每一个上都产生了性能改进。最后，证明了离线学习可以从小的、