Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
This work addresses a challenging combinatorial optimization problem in public transit design, offering incremental improvements through a hybrid approach.
The paper tackled the transit network design problem by combining a neural network policy with a modified Bee Colony Optimization metaheuristic, resulting in performance improvements of up to 20% over the neural policy alone and 53% over the original BCO algorithm on realistic instances.
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.