# Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2211.00759 · HTML: https://mlsanity.com/paper/2211.00759/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [POMO: Policy Optimization with Multiple Optima for Reinforcement Learning](https://mlsanity.com/paper/2010.16011/) (2020)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/1911.09539/) (2019)
- [Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs](https://mlsanity.com/paper/2107.10201/) (2021)
- [Learning Large Neighborhood Search Policy for Integer Programming](https://mlsanity.com/paper/2111.03466/) (2021)
- [Learn to Design the Heuristics for Vehicle Routing Problem](https://mlsanity.com/paper/2002.08539/) (2020)
- [Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search](https://mlsanity.com/paper/2005.09330/) (2020)
- [A Reinforcement Learning Approach to the Orienteering Problem with Time Windows](https://mlsanity.com/paper/2011.03647/) (2020)
- [The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems](https://mlsanity.com/paper/2201.10453/) (2022)

## Built on by (most cited first, 6 of 6)

- [New Adaptive Mechanism for Large Neighborhood Search using Dual Actor-Critic](https://mlsanity.com/paper/2601.11414/) (2026)
- [Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search](https://mlsanity.com/paper/2506.02746/) (2025)
- [When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling](https://mlsanity.com/paper/2604.01886/) (2026)
- [COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space](https://mlsanity.com/paper/2605.20618/) (2026)
- [RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning](https://mlsanity.com/paper/2606.20142/) (2026)
- [SPO: Discovering Adaptive Large Neighborhood Search Operators via Stackelberg Program Optimization](https://mlsanity.com/paper/2609.31179/) (2026)

## Abstract (arXiv)

The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS relies on the proper configuration of its selection and acceptance parameters, which is known to be a complex and resource-intensive task. To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search. The proposed method aims to learn, based on the state of the search, to configure ALNS for the next iteration to yield more effective solutions for the given optimization problem. We evaluate the proposed method on an orienteering problem with stochastic weights and time windows, as presented in an IJCAI competition. The results show that our approach outperforms vanilla ALNS, ALNS tuned with Bayesian optimization, and two state-of-the-art DRL approaches that were the winning methods of the competition, achieving this with significantly fewer training observations. Furthermore, we demonstrate several good properties of the proposed DR-ALNS method: it is easily adapted to solve different routing problems, its learned policies perform consistently well across various instance sizes, and these policies can be directly applied to different problem variants.