Multi-objective Optimization Method For Large-scale Evaluation Problems

New energy vehicles are the future development direction of the automobile industry.In order to promote the popularization of electric vehicles and facilitate people to travel,the multi-objective optimization method of location and capacity determination of Expressway charging station has high research value and application value.How to determine the charging demand of vehicles is one of the key problems in the problem of location and capacity of charging station.Firstly,the minimum charging principle is proposed for the charging demand of vehicles,and the fitness function is proposed for the determination of charging station capacity.The NSGA-Ⅱ algorithm is improved and a multi-objective optimization model with the maximum average fit of the lowest construction cost is constructed to study the location and capacity of the charging station.Then,the charging probability function is proposed,and the multi-objective optimization model is transformed by scalar method to study the multi cycle location and capacity model of Expressway charging station.The first chapter mainly presents some current research status of charging station location and capacity problem,solving algorithm and multi-objective optimization method,and puts forward the main content of this thesis.The second chapter introduces the principles and general steps of genetic algorithm and NSGA-Ⅱ algorithm,as well as some concepts of multi-objective programming solution.The third chapter mainly studies the problem of the location and capacity of expressway charging stations.First,the principle of least charging is proposed to determine the charging needs of users,and then the charging station fitness evaluation is proposed to measure the degree of matching between the capacity of the charging station and the service volume.And build a multi-objective optimization model with the goal of minimizing the construction cost of charging stations and maximizing the average adaptability of charging stations.In view of the traditional NSGA-Ⅱ algorithm’s shortcomings that the random selection operator is difficult to select outstanding individuals for the genetic process,refer to the roulette algorithm to improve the selection operator to increase the quality of understanding to obtain better solutions.After that,numerical experiments and analyses are carried out on the multi-objective model through calculation examples.The calculation results give a series of Pareto solutions.The average degree of adaptation corresponding to each solution is relatively high,which verifies the accuracy and effectiveness of the model and algorithm.Finally,a sensitivity analysis of the model is carried out.Chapter 4 mainly studies the problem of multi-period site selection and capacity determination of expressway charging stations under construction cost constraints.First,the charging probability function is proposed to describe the user’s charging behavior,and a multi-objective optimization model with the goal of maximizing the adaptability of the charging pile is established.The model is transformed by the scalarization method,which proves that the scalarization model is different from the original model.The relationship between solutions.The relationship between the solution of this model is studied,and the efficiency of the algorithm is increased.The genetic algorithm is used to solve the problem,and the optimal station construction plan is obtained.Then,in view of the long construction time of charging stations and the long construction period of the project,the overall plan is considered to be divided into multiple periods for construction,and a multi-period location and capacity model for charging stations is established with the goal of maximizing the number of charging vehicles in the total period.Finally,a numerical experiment was carried out on the calculation example using genetic algorithm,and the parameters were adjusted to further analyze the model.