Research On The Location Of Shared Bike Parking Spots Based On Improved Grey Wolf Algorithm

Since entering the 21 st century,the rapid development of the Internet economy and the sharing economy has injected new vitality into the development of shared bicycle systems.Shared bicycles have made a significant contribution to solving the "last mile" problem of urban public transportation,greatly improving the service level of urban public transportation,and becoming an indispensable part of the urban public transportation system.With the "barbaric growth" of shared bicycles,the problem of random parking has become increasingly prominent,causing huge troubles to urban management,seriously affecting the appearance of the city,and violating the nature of shared bicycles to serve the public.Therefore,the research on the location of shared bicycle parking spots appears to be of great research value.First of all,in the model of location selection for shared bicycle parking spots,this paper divides the user’s entire cycling process into three stages: starting point to find a car,cycling process,and stopping to walk to the destination.Taking into consideration factors such as demand distribution,walking willingness radius,the number of bicycles at their disposal,and parking capacity limitations,the optimization goals are to minimize the total riding time of users and minimize the total cost of bicycle operators,and establish a userbased riding trip.The location model of shared bicycle parking spots.Secondly,in the design of the bionic algorithm to solve the model,this paper aims at the defects of poor population diversity,easy to fall into local optimum,and slow convergence speed in the later stage of the standard grey wolf algorithm(GWO),and draws on the improvement of Tent chaotic sequence,spiral function,simulated annealing algorithm,Genetic algorithm and Gaussian perturbation strategy,an improved grey wolf algorithm(IGWO)is proposed,and 8 different types of benchmark functions are used for performance testing in the same environment as the standard grey wolf algorithm(GWO),genetic algorithm(GA)and particle swarm algorithm(PSO).The results show that the improved gray wolf algorithm(IGWO)has faster convergence speed,higher optimization accuracy and superior performance than the standard gray wolf algorithm(GWO),genetic algorithm(GA)and particle swarm algorithm(PSO).Finally,this paper selects an example.After setting the model parameters and basic data,the improved gray wolf algorithm(IGWO)is used to solve and analyze the entire shared bicycle parking spot location planning process,which verifies the feasibility and effectiveness of the model.