Shared bicycle parking point distribution method based on multi-objective genetic algorithm

Abstract

The invention relates to a shared bicycle parking point distribution method based on a multi-objective genetic algorithm. The method comprises the steps: S1, enabling a server to collect a plurality of current user request data within a certain time point; s2, after the server collects request data of the user, performing statistical analysis on coordinate information, destination information andnearby available parking point position information of the user; s3, selecting a competition algorithm as a selection operator, selecting selfing as a crossover operator, and selecting a double competition participation mode; s4, dividing the population into a plurality of levels by utilizing rapid non-dominated sorting, and calculating the crowding degree of the population; and s5, merging populations. The beneficial effects of the invention are that: according to the parking point distribution system based on the multi-objective genetic algorithm, the genetic algorithm and the regression algorithm are combined, the Hypervolume evaluation index is added, the improvement performance of the algorithm is discussed, the improvement performance is mainly represented by convergence and distributivity, and the execution efficiency and the execution effect of the algorithm are optimized to a certain extent.

Description

technical field [0001] The invention relates to the field of allocation of parking spots for shared bicycles, and in particular includes a method for allocating parking spots for shared bicycles based on a multi-objective genetic algorithm. Background technique [0002] With the development of foreign public bicycles gradually improving experience, coupled with the increasingly prominent urban traffic problems caused by the development of cities in our country, in this context, low-carbon environmental protection, energy saving, convenient, fast and economical and practical shared bicycles in my country born in the city. [0003] Most cities in China have serious traffic problems, and traffic jams caused by rush hours are very common in major cities. In addition, the shared bicycle industry has been a hotspot in traffic and management since its inception. The phenomenon of random parking of bicycles not only affects the city appearance, but also creates a harsh traffic envir...

AI Technical Summary

Problems solved by technology

Although shared bicycles have improved people's travel methods, their parking problems cannot be allowed to affect urban traffic

The parking problem is not only a personal problem for users. The bicycle industry has not played a guiding role in parking the vehicles for users, and the construction of parking spots is not yet mature. In addition, users have low awareness of standardized parking, which has led to the phenomenon of random parking of bicycles. Everywhere

Moreover, this phenomenon has also caused a series of problems to a certain extent: such as traffic congestion and car rental problems, which have reduced people's favorability for the shared bicycle industry; car rental problems have directly affected user experience, causing a vicious circle

Although the scheduling method based on genetic algorithm improves the scheduling efficiency and saves the scheduling cost, the selection of cross individuals is unreasonable, and the genetic performance of individuals in real number coding is not outstanding, so individuals with better fitness cannot represent one of them. Chromosomal segments with good genes

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