求解带负载约束车辆路径问题的多策略协同智能优化算法

Multi-strategy collaborative intelligent optimization algorithm for solving vehicle routing problem with load constraints

随着现代科技和社会的进步,交通运输领域中的车辆路径问题已经成为了影响各行业发展的重要因素,在这个背景下,有效的车辆路径规划变得至关重要,这其中较为典型的便是带负载约束的车辆路径问题。为此,本文提出了一种基于鲸鱼优化算法多策略协同的优化方法,该方法将鲸鱼优化算法(WOA)、模拟退火(SA)和引力搜索算法(GSA)结合应用于带负载约束的车辆路径问题,通过这一方法,降低运输成本,减少车辆的行驶距离以及提高车辆的运输效率。为验证方法有效性和可行性,我们采用CVRPLIB中的数据进行了实验,结果表明:所提算法能够有效解决带容量约束车辆路径问题,有较强的寻优能力,求解质量优于所对比算法。

With the progress of modern science and technology and society,the vehicle routing problem in the field of transportation has become an important factor affecting the development of various industries.In this context,effective vehicle routing planning has become crucial,among which the more typical is the vehicle routing problem with load constraints.Therefore,this paper proposes a multi-strategy collaborative optimization method based on whale optimization algorithm,which combines Whale optimization algorithm(WOA),simulated annealing algorithm(SA)and gravity search algorithm(GSA)to apply to vehicle routing problem with load constraints.Through this method,transportation cost is reduced,vehicle travel distance is reduced and vehicle transportation efficiency is improved.To verify the effectiveness and feasibility of the proposed method,experiments are conducted using data from CVRPLIB.The results show that the proposed algorithm can effectively solve the capacity-constrained vehicle routing problem with strong optimization capabilities,and the solution quality is superior to that of the compared algorithms.