Adaptive memetic algorithm for the vehicle routing problem with time windows

Abstract

This paper presents an adaptive memetic algorithm (AMA) to minimize the total travel distance in the NP-hard vehicle routing problem with time windows (VRPTW). Although memetic algorithms (MAs) have been proven to be very efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the AMA in which the selection scheme and the population size are adjusted during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the search space. An extensive experimental study confirms that the AMA outperforms a standard MA in terms of the convergence capabilities.