Bacterial Memetic Algorithm for Asymmetric Capacitated Vehicle-Routing Problem

Abstract

The vehicle-routing problem (VRP) has many variants, including the most accurate models of real-life transportation tasks, making it one of the most important mathematical problems in the field of logistics. Our goal was to design an algorithm that can race against the most recent solutions for VRP and capacitated VRP (CVRP), while also being applicable to real-life models with simulations of real transports. Our algorithm is a variant of the bacterial memetic algorithm (BMA), which we improve upon with novel operators and better methods for manual parameter optimization. The key to our performance is a balanced mixture of the global search of evolutionary algorithms, local search of 2-OPT variants, and the pseudo-global search of probabilistic construction algorithms. Our algorithm benefits from the advantages of all three methods resulting in fast convergence and avoidance of global minima. This is the first time BMA is applied for VRP, meaning that we had to adapt the method for the new problem. We compare our method with some of the most-used methods for VRP on the ABEFMP 1995 dataset. We provide comparison results with the coronavirus herd immunity optimizer, genetic algorithm, hybridization of genetic algorithm with neighborhood search, firefly algorithm, enhanced firefly algorithm, ant colony optimization, and variable neighborhood search. Our algorithm performed better on all data instances, yielding at least a 30% improvement. We present our best result on the Belgium 2017 dataset for future reference. Finally, we show that our algorithm is capable of handling real-life models. Here we are also illustrating the significance of the different parameters.