Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems. It learns to perform flexible k-opt exchanges based on a tailored action factorization method and a customized recurrent dual-stream decoder. As a pioneering work to circumvent the pure fea...
Saved in:
Main Authors: | MA, Yining, CAO, Zhiguang, CHEE, Yew Meng |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8399 https://ink.library.smu.edu.sg/context/sis_research/article/9402/viewcontent/2310.18264.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Efficient neural collaborative search for pickup and delivery problems
by: KONG, Detian, et al.
Published: (2024) -
Efficient neural neighborhood search for pickup and delivery problems
by: MA, Yining, et al.
Published: (2022) -
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
by: Chinneck, John W.
Published: (2017) -
LEARNING-TO-SEARCH APPROACHES FOR VEHICLE ROUTING PROBLEMS USING DEEP REINFORCEMENT LEARNING
by: MA YINING
Published: (2024) -
Collaboration! Towards robust neural methods for routing problems
by: ZHOU, Jianan, et al.
Published: (2024)