期刊
GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
卷 -, 期 -, 页码 1093-1100出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2908812.2908942
关键词
Hyper-heuristic; VRP; Genetic Programming
资金
- EPSRC [EP/J021628/1] Funding Source: UKRI
Hyper-heuristic methods for solving vehicle routing problems (VRP) have proved promising on a range of data. The vast majority of approaches apply selective hyper-heuristic methods that iteratively choose appropriate heuristics from a fixed set of pre-defined low-level heuristics to either build or perturb a candidate solution. We propose a novel hyperheuristic called GP-MHH that operates in two stages. The first stage uses a novel Genetic Programming (GP) approach to evolve new high quality constructive heuristics; these can be used with any existing method that relies on a candidate solution(s) as its starting point. In the second stage, a perturbative hyper-heuristic is applied to candidate solutions created from the new heuristics. The new constructive heuristics are shown to outperform existing low-level heuristics. When combined with a naive perturbative hyperheuristic they provide results which are both competitive with known optimal values and outperform a recent method that also designs new heuristics on some standard benchmarks. Finally, we provide results on a set of rich VRPs, showing the generality of the approach.
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