Hybrid Genetic Algorithms for the Asymmetric Distance-Constrained Vehicle Routing Problem

We aim to suggest a simple genetic algorithm (GA) and other four hybrid GAs (HGAs) for solving the asymmetric distance-constrained vehicle routing problem (ADVRP), variant of (VRP). The VRP is difficult NP-hard optimization that has numerous real-life applications. aims find an optimal tour least total distance (or cost) provide service n customers nodes or cities) utilizing m vehicles so every starts journey from ends at depot (headquarters) visits customer only once. many variations, we consider ADVRP this study, where traveled by must not exceed predefined maximum distance. proposed GA uses random initial population followed sequential constructive crossover swap mutation. HGAs enhance solution using 2-opt search method incorporate local technique along with immigration procedure obtain effective ADVRP. Experiments have been conducted among suggested several restricted unrestricted instances on TSPLIB vehicles. Our experiments claim methods are very effective. Finally, reported comparative study between our best HGA state-of-the-art capacitated found better than instances.