An external archive guided NSGA-II algorithm for Multi-Depot Green Vehicle Routing Problem

Abstract

The paper proposes a new elitist non-dominated sorting genetic algorithm II, called External Archive Guided Elitist Non-dominated Sorting Genetic Algorithm II (EAG-NSGA-II) to solve the multi-depot green vehicle routing problem (MDGVRP). In the proposed algorithm, the elitist nondominated sorting genetic algorithm II (NSGA-II) is incorporated with adaptive local search to improve the solution0 s quality and greatly accelerate the convergence. To ensure a good balance between convergence and diversity, we employ the external archive based on adaptive epsilon dominance. The experiments are conducted over famous 11 Cordeau0 s data sets and compared against three well-known algorithms, including Strength Pareto evolutionary algorithm (SPEA2), Elitist non-dominated sorting genetic algorithm (NSGA-II), and Multi-objective evolutionary algorithm based on decomposition (MOEA/D). The results indicate that the proposed algorithm is highly competitive and outperforms the selected state-of-the-art multiobjective optimization algorithms.