Neural Bee Colony Optimization: A Case Study in Public Transit Network Design

• URL: http://arxiv.org/abs/2306.00720v1
• Date: Thu, 18 May 2023 17:48:46 GMT
• Title: Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
• Authors: Andrew Holliday, Gregory Dudek
• Abstract summary: 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.
• Score: 11.490474551585217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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