A Particle Swarm Optimization hyper-heuristic for the Dynamic Vehicle Routing Problem
This is an incremental improvement for logistics and routing optimization, specifically targeting dynamic vehicle routing scenarios.
The paper tackles the Dynamic Vehicle Routing Problem by using a linear model to predict and select the best Particle Swarm Optimization algorithm based on initial data, improving average results by choosing the appropriate algorithm in 82% of significant cases.
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.