  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.
