In this work we explore the combination of metaheuristics and learned neural
network solvers for combinatorial optimization. We do this in the context of
the transit network design problem, a uniquely challenging combinatorial
optimization problem with real-world importance. We train a neural network
policy to perform single-shot planning of individual transit routes, and then
incorporate it as one of several sub-heuristics in a modified Bee Colony
Optimization (BCO) metaheuristic algorithm. Our experimental results
demonstrate that this hybrid algorithm outperforms the learned policy alone by
up to 20% and the original BCO algorithm by up to 53% on realistic problem
instances. We perform a set of ablations to study the impact of each component
of the modified algorithm.

本文介绍了将元启发式算法和神经网络求解器相结合应用于组合优化中，以运输网络设计问题为例进行了探索，通过将单次规划的神经网络策略训练和纳入到修改后的蜜蜂群优化算法中，实验结果表明，这种混合算法在现实问题实例上的性能比单独的学习策略提高了多达 20％，比原始蜜蜂群优化算法提高了多达 53％，并对修改后的算法的每个组件进行了一系列削减实验以研究其影响。