Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation
This work addresses combinatorial optimization problems for researchers and practitioners, representing an incremental advancement in neural improvement heuristics.
The authors tackled combinatorial optimization problems by introducing Limited Rollout Beam Search (LRBS), a beam search strategy for deep reinforcement learning-based improvement heuristics, which achieved optimality gaps that outperform existing improvement heuristics and narrowed the gap with state-of-the-art constructive methods on the Euclidean Traveling Salesperson Problem and variants.
We introduce Limited Rollout Beam Search (LRBS), a beam search strategy for deep reinforcement learning (DRL) based combinatorial optimization improvement heuristics. Utilizing pre-trained models on the Euclidean Traveling Salesperson Problem, LRBS significantly enhances both in-distribution performance and generalization to larger problem instances, achieving optimality gaps that outperform existing improvement heuristics and narrowing the gap with state-of-the-art constructive methods. We also extend our analysis to two pickup and delivery TSP variants to validate our results. Finally, we employ our search strategy for offline and online adaptation of the pre-trained improvement policy, leading to improved search performance and surpassing recent adaptive methods for constructive heuristics.