Application Of Improved Bacterial Foraging Algorithm In Urban Rail Transit Scheduling

With the rapid development of social economy and cities,a large number of rural populations are pouring into cities to find job opportunities,travel demand for urban residents has increased dramatically,In order to alleviate the increasingly serious city traffic pressure,city managers will build urban rail transit as the primary choice for relieving traffic pressure.Compared with traditional urban public transport,urban rail transit has the advantages of high traffic volume,fast speed,short time,wide audience,and environmental protection.Train scheduling is the core work of operating companies of an urban rail transit and plays the crucial role in reducing the operating costs,improving operating efficiency and service levels.The article's rail transit scheduling optimization work is mainly to optimize the train scheduling scheme and dynamically adjust the train dispatching scheme according to the actual passenger flow situation.However,it is difficult to optimize the multi-objective nonlinear NP problem by using traditional mathematical methods,and the main limiting factors are large spatial solution,high dimensionality and complex constraints.With the development of intelligent computing,the intelligent optimization algorithm can effectively solve the public transportation scheduling optimization problem.Based on the analysis of the bacterial foraging algorithm,the improved bacterial foraging algorithm is combined with the train scheduling mathematical model to solve the optimized train scheduling scheme.The main work of the article is as follows:(1)In-depth study of bacterial foraging algorithms.The bionics foundation,structure principle,algorithm flow and main operations of the bacterial foraging optimization algorithm are discussed,and the influence of parameters on the performance of the algorithm is analyzed.This paper proposes a Gauss-Cauchy adaptive bacterial foraging algorithm based on Log-Linear model.Firstly,the Log-Linear model is introduced into the basic algorithm to optimize the trend and migration of bacteria.Secondly,the strategy of adaptively adjusting the bacterial step size is introduced into the algorithm to improve the search range and optimization precision of the bacteria.Finally,the Gauss-Cauchy mutations increase the diversity of bacterial populations.(2)Analyze the spatial and temporal distribution characteristics of passenger flow on each line in urban rail transit network.Research many factors what affect the train scheduling.On the basis of analyzing the factors what affect the interests of both operating companies and passengers,establishing optimization objectives and constraint conditions,building the scheduling optimization mathematical models.(3)The process of combining the improved algorithm with the scheduling mathematical model is introduced in detail,including in-depth analysis of the initialization of bacteria,the setting of various parameters,and the development of coding schemes.Then the urban rail transit scheduling optimization strategy based on the bacterial foraging optimization algorithm is applied to the simulation data of a city rail transit instance data.Finally,theoretical and experimental analysis shows that the algorithm given in the paper is feasible and efficient,and has certain theoretical and practical value.