关键词: 车辆路径问题, 多目标优化, 差分进化算法, 邻接信息矩阵, 精英碎片保护策略

Abstract: Aiming at multi-objective vehicle routing problem with time windows, proposes Differential Evolution algorithm integrating Mutation Strategy and Adjacent Information (DE-MSAI). First, four mutation operators are devised by employing elitist sampling strategy to increase algorithm search breadth. Second, customer adjacency information matrix is established to guide g targeted neighborhood search, improving local optimization efficiency. Finally, simulated annealing criterion is adopted to accept inferior solutions with certain probability. If Pareto non-dominated solution set remains unimproved beyond preset iterations during optimization, elite fragment protection strategy is activated to perturb a randomly selected solution from non-dominated set, maintaining population diversity. Simulation experiments based on Solomon benchmark instances show that proposed algorithm controls solving error within 0.07 compared to hybrid crow search algorithm, and outperforms clustering-based large neighborhood search algorithm in most cases, achieving an average reduction of 4.51 in route deviation metrics, verifying its effectiveness.

Key words: vehicle routing problem, multi-objective optimization, Differential Evolution &#40, DE&#41, algorithm