New Selection Schemes in a Memetic Algorithm for the Vehicle Routing Problem with Time Windows

Abstract

This paper presents an extensive study on the pre- and post-selection schemes in a memetic algorithm (MA) for solving the vehicle routing problem with time windows. In the MA, which is a hybridization of the genetic and local optimization algorithms, the population of feasible solutions evolves with time. The fitness of the individuals is measured based on the fleet size and the total distance traveled by the vehicles servicing a set of geographically scattered customers. Choosing the proper selection schemes is crucial to avoid the premature convergence of the search, and to keep the balance between the exploration and exploitation during the search. We propose new selection schemes to handle these issues. We present how the various selection schemes affect the population diversity, convergence of the search and solutions quality. The quality of the solutions is measured as their proximity to the best currently-known feasible solutions. We present the experimental results for the well-known Gehring and Homberger’s benchmark tests.