GMMA: GPU-based multiobjective memetic algorithms for vehicle routing problem with route balancing

Abstract

A multiobjective optimization problem called a vehicle routing problem with route balancing (VRPRB) is studied. VRPRB extends traditional VRPs by considering two objectives simultaneously. The first objective is the minimization of the total traveling cost and the second one tries to ensure the balance among multiple routes. Different from another commonly used balancing objective, namely, the minimization of the difference between the maximal and minimal route cost, the objective we introduce is the minimization of the maximal route cost. Such setting can effectively avoid the occurrence of distorted solutions. In order to find Pareto-optimal solutions of VRPRB, we develop a multiobjective memetic algorithm (MMA), which integrates a problem-specific local search procedure into a multiobjective evolutionary algorithm. The MMA is further enhanced by using parallel computations on GPU devices. A simple version and a revised version of GPU-based MMAs are proposed and implemented on the CUDA platform. All the algorithms are tested on the benchmark instances to demonstrate their efficacy and effectiveness. Furthermore, the performances of CPU-based and GPU-based algorithms are analyzed.