DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
This addresses the problem of scaling DRL solvers to large graphs for researchers and practitioners in combinatorial optimization, representing a novel method for a known bottleneck.
The paper tackles the scalability challenge of deep reinforcement learning (DRL) solvers for NP-hard combinatorial optimization problems, such as the Traveling Salesman Problem (TSP), by proposing DIMES, which introduces a compact continuous space for parameterizing solution distributions and a meta-learning framework, resulting in outperformance over recent DRL-based methods on large benchmark datasets.
Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers can only scale to a few hundreds of nodes for combinatorial optimization problems on graphs, such as the Traveling Salesman Problem (TSP). This paper addresses the scalability challenge in large-scale combinatorial optimization by proposing a novel approach, namely, DIMES. Unlike previous DRL methods which suffer from costly autoregressive decoding or iterative refinements of discrete solutions, DIMES introduces a compact continuous space for parameterizing the underlying distribution of candidate solutions. Such a continuous space allows stable REINFORCE-based training and fine-tuning via massively parallel sampling. We further propose a meta-learning framework to enable the effective initialization of model parameters in the fine-tuning stage. Extensive experiments show that DIMES outperforms recent DRL-based methods on large benchmark datasets for Traveling Salesman Problems and Maximal Independent Set problems.