带硬时间窗模糊车辆路径问题的多目标优化

摘要

Aiming at the vehicle routing problem with hard time windows and multiple fuzzy characteristics,a multi-objective fuzzy expected model is designed based on fuzzy credibility theory,and an adaptive hybrid Multi-objective Particle Swarm Optimization(MOPSO) is proposed to solve the fuzzy vehicle routing model.The algorithm puts forward a particle encoding method according as phase-space,and designs a double archiving mechanism which stores the non-dominated solutions and excellent infeasible solutions separately.It also introduces adaptive strategies on local search,mutation and selection for particle global guide.The compareative experiments with multi-objective evolutionary algorithm verify that the method is capable of getting more excellent Pareto sets.%针对带硬时间窗车辆路径问题的多重模糊性,基于模糊可信性理论建立多目标模糊期望值模型,提出求解该问题的自适应混合多目标粒子群优化算法.该算法根据相位空间的思想给出一种实数编码方式,设计双存档机制,分别存储演化过程中产生的非支配解和有益不可行解,并引入自适应局部搜索、变异和粒子全局向导选择策略.仿真实验结果表明,与多目标进化算法相比,该算法可以获得更优的Pareto解集.