Learning to Select Promising Initial Solutions for Large Neighborhood Search-Based Multi-Agent Path Finding

Huber, Marc; Raidl, Günther R.; Blum, Christian

doi:10.1007/978-3-031-82949-9_22

by Marc Huber, Günther R. Raidl, Christian Blum

Reference:

Learning to Select Promising Initial Solutions for Large Neighborhood Search-Based Multi-Agent Path FindingMarc Huber, Günther R. Raidl, Christian BlumComputer Aided Systems Theory – EUROCAST 2024 (Alexis Quesada-Arencibia, Michael Affenzeller, Roberto Moreno-Díaz, eds.), volume 15172 of LNCS, pages 236-250, 2025, Springer.

Bibtex Entry:

@inproceedings{huber-25,
author = {Huber, Marc and Raidl, Günther R. and Blum, Christian},
title= {Learning to Select Promising Initial Solutions for Large Neighborhood Search-Based Multi-Agent Path Finding},
booktitle = {Computer Aided Systems Theory -- EUROCAST 2024},
editor = {Quesada-Arencibia, Alexis and Affenzeller, Michael and Moreno-D{\'i}az, Roberto},
series = {LNCS},
volume = 15172,
pages = {236-250},
publisher = {Springer},
year = {2025},
doi = {10.1007/978-3-031-82949-9_22},
url = {https://www.ac.tuwien.ac.at/files/pub/huber-24.pdf}
}