论文标题
用于动态车辆路由问题的粒子群优化超高热
A Particle Swarm Optimization hyper-heuristic for the Dynamic Vehicle Routing Problem
论文作者
论文摘要
本文介绍了一种根据给定问题实例的最初可用数据，为动态车辆路由问题选择基于粒子群优化的方法。优化算法是根据对该数据进行训练的线性模型和优化算法获得的相对结果的预测来选择的。实现的结果表明，通过在82％的有效情况下选择适当的算法，可以在基准实例集中获得的平均结果（在基准实例集获得的平均结果）中使用。基于解决动态车辆路由问题的两个领先的多链粒子群优化算法用作基本优化算法：Khouadjia的等等。多种环境多损伤优化器和作者的2-相多变量粒子群优化。
This paper presents a method for choosing a Particle Swarm Optimization based optimizer for the Dynamic Vehicle Routing Problem on the basis of the initially available data of a given problem instance. The optimization algorithm is chosen on the basis of a prediction made by a linear model trained on that data and the relative results obtained by the optimization algorithms. The achieved results suggest that such a model can be used in a hyper-heuristic approach as it improved the average results, obtained on the set of benchmark instances, by choosing the appropriate algorithm in 82% of significant cases. Two leading multi-swarm Particle Swarm Optimization based algorithms for solving the Dynamic Vehicle Routing Problem are used as the basic optimization algorithms: Khouadjia's et al. Multi-Environmental Multi-Swarm Optimizer and authors' 2--Phase Multiswarm Particle Swarm Optimization.
