论文标题
一种基于自适应的模拟退火和加固的新型超高式，用于电容的电动汽车路由问题
A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem
论文作者
论文摘要
由于货车数量的增加，电动汽车（EV）已在城市地区采用，以减少环境污染和全球变暖。但是，路由最后一英里物流的轨迹仍在继续影响社会和经济可持续性时仍然存在缺陷。因此，在本文中提出了一种称为超增压自适应模拟退火（HHASA $ _ {rl} $）的超女性（HH）方法。它由多军匪徒方法和自适应模拟退火（SA）元启发式算法组成，用于求解称为电容的电动汽车路由问题（CEVRP）的问题。由于充电站数量有限和电动汽车的旅行范围，因此电动汽车必须提前为电池充电时刻，并减少旅行时间和成本。 HH实施的HH改善了多个最低最低知名解决方案，并为IEEE WCCI2020竞赛的拟议基准测试获得了一些高维实例的最佳平均值。
Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA$_{RL}$) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The HH implemented improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.
