Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems
- URL: http://arxiv.org/abs/2501.00852v1
- Date: Wed, 01 Jan 2025 14:29:54 GMT
- Title: Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems
- Authors: Van Quang Nguyen, Quoc Chuong Nguyen, Thu Huong Dang, Truong-Son Hy,
- Abstract summary: HDCARP is an extension of the Capacitated Arc Routing Problem (CARP)
We propose a fast to efficiently address the computational challenges of HDCARP.
We name this hybrid algorithm as the Hybrid Reinforcement Learning and Heuristic Algorithm for Directed Arc Routing (HRDA)
- Score: 4.2873412319680035
- License:
- Abstract: The Hierarchical Directed Capacitated Arc Routing Problem (HDCARP) is an extension of the Capacitated Arc Routing Problem (CARP), where the arcs of a graph are divided into classes based on their priority. The traversal of these classes is determined by either precedence constraints or a hierarchical objective, resulting in two distinct HDCARP variants. To the best of our knowledge, only one matheuristic has been proposed for these variants, but it performs relatively slowly, particularly for large-scale instances (Ha et al., 2024). In this paper, we propose a fast heuristic to efficiently address the computational challenges of HDCARP. Furthermore, we incorporate Reinforcement Learning (RL) into our heuristic to effectively guide the selection of local search operators, resulting in a hybrid algorithm. We name this hybrid algorithm as the Hybrid Reinforcement Learning and Heuristic Algorithm for Directed Arc Routing (HRDA). The hybrid algorithm adapts to changes in the problem dynamically, using real-time feedback to improve routing strategies and solution's quality by integrating heuristic methods. Extensive computational experiments on artificial instances demonstrate that this hybrid approach significantly improves the speed of the heuristic without deteriorating the solution quality. Our source code is publicly available at: https://github.com/HySonLab/ArcRoute
Related papers
- Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search [61.08720171136229]
We present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques.
Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms.
Our techniques bring a new way of approaching the problem and a new level of precision to the field.
arXiv Detail & Related papers (2024-07-22T23:24:03Z) - An Efficient Learning-based Solver Comparable to Metaheuristics for the
Capacitated Arc Routing Problem [67.92544792239086]
We introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics.
First, we propose direction-aware facilitating attention model (DaAM) to incorporate directionality into the embedding process.
Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy.
arXiv Detail & Related papers (2024-03-11T02:17:42Z) - Enhancing Column Generation by Reinforcement Learning-Based
Hyper-Heuristic for Vehicle Routing and Scheduling Problems [9.203492057735074]
Column generation (CG) is a vital method to solve large-scale problems by dynamically generating variables.
We propose a reinforcement learning-based hyper-heuristic framework, dubbed RLHH, to enhance the performance of CG.
arXiv Detail & Related papers (2023-10-15T00:05:50Z) - Bringing regularized optimal transport to lightspeed: a splitting method
adapted for GPUs [9.297785393486976]
We present an efficient algorithm for regularized optimal transport.
In contrast to previous methods, we use the Douglas-Rachford splitting technique to develop an efficient solver that can handle a broad class of regularizers.
arXiv Detail & Related papers (2023-05-29T12:04:55Z) - Stochastic Unrolled Federated Learning [85.6993263983062]
We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning.
Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
arXiv Detail & Related papers (2023-05-24T17:26:22Z) - Solving the vehicle routing problem with deep reinforcement learning [0.0]
This paper focuses on the application of RL for the Vehicle Routing Problem (VRP), a famous problem that belongs to the class of NP-Hard problems.
In a second phase, the neural architecture behind the Actor and Critic has been established, choosing to adopt a neural architecture based on the Convolutional neural networks.
Experiments performed on a wide range of instances show that the algorithm has good generalization capabilities and can reach good solutions in a short time.
arXiv Detail & Related papers (2022-07-30T12:34:26Z) - Reinforcement Learning for Branch-and-Bound Optimisation using
Retrospective Trajectories [72.15369769265398]
Machine learning has emerged as a promising paradigm for branching.
We propose retro branching; a simple yet effective approach to RL for branching.
We outperform the current state-of-the-art RL branching algorithm by 3-5x and come within 20% of the best IL method's performance on MILPs with 500 constraints and 1000 variables.
arXiv Detail & Related papers (2022-05-28T06:08:07Z) - DAAS: Differentiable Architecture and Augmentation Policy Search [107.53318939844422]
This work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them.
Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm.
arXiv Detail & Related papers (2021-09-30T17:15:17Z) - Adaptive Approach For Sparse Representations Using The Locally
Competitive Algorithm For Audio [5.6394515393964575]
This paper presents an adaptive approach to optimize the gammachirp's parameters.
The proposed method consists of taking advantage of the LCA's neural architecture to automatically adapt the gammachirp's filterbank.
Results demonstrate an improvement in the LCA's performance with our approach in terms of sparsity, reconstruction quality, and convergence time.
arXiv Detail & Related papers (2021-09-29T20:26:16Z) - Phase Retrieval using Expectation Consistent Signal Recovery Algorithm
based on Hypernetwork [73.94896986868146]
Phase retrieval is an important component in modern computational imaging systems.
Recent advances in deep learning have opened up a new possibility for robust and fast PR.
We develop a novel framework for deep unfolding to overcome the existing limitations.
arXiv Detail & Related papers (2021-01-12T08:36:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.