  In recent years, combining neural networks with local search heuristics has
become popular in the field of combinatorial optimization. Despite its
considerable computational demands, this approach has exhibited promising
outcomes with minimal manual engineering. However, we have identified three
critical limitations in the empirical evaluation of these integration attempts.
Firstly, instances with moderate complexity and weak baselines pose a challenge
in accurately evaluating the effectiveness of learning-based approaches.
Secondly, the absence of an ablation study makes it difficult to quantify and
attribute improvements accurately to the deep learning architecture. Lastly,
the generalization of learned heuristics across diverse distributions remains
underexplored. In this study, we conduct a comprehensive investigation into
these identified limitations. Surprisingly, we demonstrate that a simple
learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA)
learned heuristics in terms of performance and generalizability. Our findings
challenge prevailing assumptions and open up exciting avenues for future
research and innovation in combinatorial optimization.
