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。

神经蜜蜂群优化：公共交通网络设计案例研究

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

Ongoing traffic changes, including those triggered by the COVID-19 pandemic,
reveal the necessity to adapt our public transport systems to the ever-changing
users' needs. This work shows that single and multi objective stances can be
synergistically combined to better answer the transit network design problem
(TNDP). Single objective formulations are dynamically inferred from the rating
of networks in the approximated (multi-objective) Pareto Front, where a
regression approach is used to infer the optimal weights of transfer needs,
times, distances, coverage, and costs. As a guiding case study, the solution is
applied to the multimodal public transport network in the city of Lisbon,
Portugal. The system takes individual trip data given by smartcard validations
at CARRIS buses and METRO subway stations and uses them to estimate the
origin-destination demand in the city. Then, Genetic Algorithms are used,
considering both single and multi objective approaches, to redesign the bus
network that better fits the observed traffic demand. The proposed TNDP
optimization proved to improve results, with reductions in objective functions
of up to 28.3%. The system managed to extensively reduce the number of routes,
and all passenger related objectives, including travel time and transfers per
trip, significantly improve. Grounded on automated fare collection data, the
system can incrementally redesign the bus network to dynamically handle ongoing
changes to the city traffic.

通过基于遗传算法的多目标和单目标方法，以及公共交通系统中自动收费数据为基础，设计一种能够动态适应城市交通变化、减少线路数量和改善乘客通勤体验的交通网络设计问题优化方案。