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Research On The Method Of Train Operation Diagram Based On Differential Evolution Algorithm

Posted on:2016-06-23Degree:MasterType:Thesis
Country:ChinaCandidate:L YangFull Text:PDF
GTID:2382330572465733Subject:Electronic and communication engineering
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This paper mainly use train diagram as the research object,the train diagram is used to express train in section of railway operation and at the station to or through the time of technical documents.It provides the train number range occupied by the program,train at each station of arrival and departure or through time of the train running time in the interval of the train in the station control the time and the locomotive routing,train weight and length is the basis of the organization of railway operation.The train diagram is a diagram of the train running time table,which provides the train at a certain time in the range of operation and in the station,and through the station.The train diagram is a diagram of the time and space relationship of the train running,which means that the train is running at different intervals and the two-dimensional line drawing at each station.In the study,the use of differential evolution algorithm is one of the optimization methods to improve the train operation diagram,and the differential evolution algorithm is a hot topic in the field of intelligent optimization algorithm.Through in-depth analysis and Research on the internal mechanism and evolution law of the algorithm,the algorithm is improved.In the aspects of global convergence,convergence speed and so on,further improve the algorithm's optimization performance,which has important theoretical and practical significance.The train running diagram is more efficient to meet the demand of transportation by using differential evolution algorithm.In this paper,based on the research of differential evolution algorithm,the model of the non model adaptation and process optimization is introduced,and the optimal design of the train diagram is designed by using the differential evolution algorithm.The specific contents of this paper are as follows:firstly,the basic principle of differential evolution algorithm and the research status of the domestic and foreign research are introduced in detail.Through the analysis of the differential evolution algorithm,the differential evolution algorithm is described as a control system,which makes the train running diagram better applied to the efficient transportation work.Then,based on the model,the differential evolution algorithm is proposed,which is based on the model of adaptive control,and the difference of the algorithm is proposed.The algorithm is based on the model.The algorithm is based on the model.Using the test function,the simulation results show that the proposed algorithm has better performance than the traditional differential evolution algorithm,while the two improved differential evolution algorithm has the advantage of the different test functions.For the train operation diagram,mainly in order to adapt to the increase in the capacity of the railway transport capacity,and make full use of transport capacity,to further improve the quality of service.In running diagram of full implementation of the "three kinds of mixed operation"train new model.This model fully embodies the reasonable structure,velocity matching principle,to meet the passenger's choice of different speed grade,different fares,railway convenience Limin of major initiatives.Finally,considering the characteristics of the two methods and the setting value of the optimization process,the process optimization idea is further introduced.The tracking object and the setting value are proposed.Simulation results show that the algorithm has a further improvement in the optimal value,optimization stability,searching speed,etc.,and further optimization in the train operation diagram shows the feasibility and effectiveness of the improved algorithm.
Keywords/Search Tags:Differential evolution algorithm(DE), Model-free adaptive control(MFAC), The variation rate of average fitness, Population diversity, Optimal control
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