An Improved Cat Swarm Optimization For Multi-depot And Multi-type Vehicle Routing Problem

The economic progress has brought many opportunities for logistics companies,and the scale has continued to increase.The distribution model of single depot and single vehicle has been unable to meet large-scale distribution needs.Therefore,its service model is constantly changing.At present,the service modes of multiple parking lots and models are more and more widely used,and there is still much room for improvement in the method and ability of solving this problem.Considering the above factors,it is of great significance to solve the multi-depot and multi-type vehicle routing problem.To this end,the vehicle routing problem and its solving method are combed and summarized,and a mathematical model is constructed;then,an improved cat swarm optimization(ICSO)is proposed to find the solution to the problem.(1)Analyze problems and build models.The main characteristics of the vehicle routing problem are described,and the mathematical model of the multi-depot and multi-type vehicle routing problem and the multi-depot and multi-type vehicle routing problem with time windows and simultaneous pickup and delivery are formulated.(2)Design and improve CSO.Based on the established mathematical model and the characteristics of the vehicle routing problem,an ICSO was designed,including algorithm's coding method,mutation operator,search mode,tracking mode,etc.,and specific steps of algorithm solving were formulated.(3)Test and analyze the performance of the ICSO.The performance of the improved cat swarm optimization was verified by testing and solving different examples in two sets of experiments and comparing the results with other algorithms.(4)The performance and parameters of the ICSO are analyzed.Firstly,the effect of simulated annealing algorithm on the performance of the CSO was analyzed.Then,by setting different population sizes,memory pool capacity and grouping rate,the solution results under different parameter values were analyzed to determine impact of the three groups of parameters for solving performance of CSO.Experiments show that for two models,compared with existing methods,the ICSO has better solution performance,faster convergence speed,higher solution quality,and stronger stability.Finally,the results of parameter analysis show that compared with the population size and mixture ratio,changes in the memory pool capacity have a greater impact on the algorithm's solution performance.This research provides a corresponding reference scheme for solving the complex optimization problem of multi-depot and multi-type vehicle routing problem,and also provides a reference for using intelligent algorithms to solve complex optimization problems.