dc.contributor.author | Liu, S | en_US |
dc.contributor.author | Chen, J | en_US |
dc.contributor.author | Weiszer, M | en_US |
dc.contributor.author | 2022 IEEE 11th International Conference on Intelligent Systems (IS) | en_US |
dc.date.accessioned | 2023-03-03T09:52:32Z | |
dc.date.issued | 2022-10-12 | en_US |
dc.identifier.isbn | 9781665456562 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/84759 | |
dc.description.abstract | To solve general multi-objective multigraph shortest path problems, this paper proposes an algorithm (MOMGA*) that incorporates an online likely-admissible learning-based heuristic function to accelerate the solution-finding process. MOMGA∗ is an extended and generalised version of the airport multi-objective A∗ (AMOA*) algorithm that is tailored for a specific application problem. The online heuristic function is added and developed using artificial neural networks that estimate the costs between two nodes based on their metrics. To implement this metric-based prediction, a graph embedding technique is adopted to learn node feature representations. Results on a range of benchmark multi-objective multigraphs show that (i) in the absence of heuristic information, MOMGA∗ can deliver the same Pareto optimal solutions as AMOA∗ does, while requiring less computational time, and (ii) empowered by the likely-admissible learning- based heuristics, MOMGA∗ is able to provide a set of optimal and near-optimal solutions and strike a good balance between optimality and tractability. | en_US |
dc.title | Multi-objective Multigraph A∗ Search with Learning Heuristics based on Node Metrics and Graph Embedding | en_US |
dc.type | Conference Proceeding | |
dc.rights.holder | © 2022 IEEE | |
dc.identifier.doi | 10.1109/IS57118.2022.10019653 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing::Engineering and Physical Sciences Research Council | en_US |
qmul.funder | TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing::Engineering and Physical Sciences Research Council | en_US |