关联运输调度问题的混合混沌量子算法研究

Hybrid Chaotic Quantum Evolutionary Algorithm for Incident Vehicle Routing Problem

考虑实际生活中商品供应商具有严格的营业时间限制、客户的个性化送货时间预设等因素,建立带客户硬时间窗、车场硬时间窗、多车场多车型等约束的关联运输调度问题模型。针对量子进化算法计算时间长、收敛速度慢以及容易出现早熟等问题,采用混沌初始化方法产生初始种群,使种群具有较好的多样性;采用简单量子旋转门更新当前种群中的非最优个体,减少算法的计算时间;提出混合混沌搜索策略提高算法的收敛速度和全局搜索能力,构造了混合混沌量子进化算法。对50客户规模的算例进行仿真表明提出的IVRP优于一般的VRP,可节约大量成本,证明其模型的有效性,且该算法在收敛速度和寻优结果两方面略优于自适应遗传算法和量子算法。

Considering these factors,such as suppliers have strict operating time limit,customers have the preset personalized delivery time,etc.,establishing the incident vehicle routing problem（ IVRP） mathematical model based on clients＇ hard time windows,depots＇ hard time windows,heterogeneous vehicles etc. In order to reduce computation time,speed up convergence and restrain premature phenomena of quantum evolutionary algorithm（ QEA）. Using the chaotic initialization method to generate initial population can improve diversity,adapting quantum rotation gate to update non-optimal individuals of population to reduce the computation time,moreover,hybrid chaotic search strategy to speed up its convergence and enhance its global search ability,thus the hybrid chaotic quantum evolutionary algorithm（ HCQEA） was constructed. Using this algorithm to solve 50 clients IVRP model.Experiments show that IVRP is superior to general VRP,it can save cost greatly. As a whole,HCQEA is slightly better than AGA and QEA in convergence speed and optimal results,and the proposed model is effective.