CN-121998216-A - Parking lot recommendation method based on improved ant colony algorithm

CN121998216ACN 121998216 ACN121998216 ACN 121998216ACN-121998216-A

Abstract

The invention discloses a parking lot recommending method based on an improved ant colony algorithm, which comprises the steps of S1, setting ant quantity and a pheromone matrix, determining weight parameters of a heuristic function, S2, in the current iteration, each ant starts from a starting point, selects the next node until reaching a destination by adopting a roulette strategy according to the pheromone concentration among nodes and the heuristic function value, and forms a complete path, wherein in the path constructing process, partial ants execute a random walking strategy to conduct global exploration, S3, after all ants complete the path construction, the pheromone matrix is uniformly updated according to the path quality of each ant, and the updating process does not comprise the pheromone volatilizing step, S4, repeatedly executing the steps S2 to S3 until iteration termination conditions are met, and outputting the global optimal path in all iterations as a recommending result, wherein the recommending result at least comprises target parking lots, distance and transit time information. The method is suitable for recommending the parking lot.

Inventors

LIU WEI
Gong Zichen
JIA XINHUI

Assignees

无锡学院

Dates

Publication Date: 20260508
Application Date: 20260203

Claims (8)

1. The parking lot recommendation method based on the improved ant colony algorithm is characterized by comprising the following steps of: S1, setting ant quantity and an pheromone matrix, and determining weight parameters of heuristic functions; S2, in the current iteration, each ant starts from a starting point, selects the next node by adopting a roulette strategy according to the pheromone concentration among the nodes and the heuristic function value until reaching the end point, and forms a complete path, wherein in the path construction process, part of ants execute a random walking strategy to perform global exploration; S3, after all ants complete path construction, uniformly updating the pheromone matrix according to the path quality of each ant, wherein the updating process does not comprise a pheromone volatilization step; s4, repeatedly executing the steps S2 to S3 until the iteration termination condition is met, and outputting the globally optimal paths in all iterations as recommended results, wherein the recommended results at least comprise target parking lots, distance and transit time information.
2. The improved ant colony algorithm-based parking lot recommendation method of claim 1, wherein the heuristic function is: ; in the formula, As a function of the heuristic properties, 、、 Are all the weight coefficients of the two-dimensional space model, As the distance from the i node to the j node, As the time from node i to node j, Is a node To the node Traffic lights of (a) are counted.
3. The improved ant colony algorithm-based parking lot recommendation method according to claim 2, wherein the weight parameter is determined according to a recommendation mode selected by a user, wherein the recommendation mode includes a distance priority mode, a transit time priority mode, and a comprehensive equalization mode.
4. The improved ant colony algorithm based parking lot recommendation method of claim 1, wherein the random walk strategy is: ; wherein X (t) is a set of steps of the ant random walk, cumssum is a calculated sum, t is a number of steps of the random walk, and r represents a random function.
5. The improved ant colony algorithm-based parking lot recommendation method as claimed in claim 1, wherein the calculation method of the probability of selecting the node i as the next node in the roulette strategy is as follows: ; in the formula, Is the probability of selecting a node i, Is the fitness value of node i, and N is the total number of nodes.
6. The parking lot recommendation method based on the improved ant colony algorithm according to claim 1, wherein updating the pheromone matrix is: ; in the formula, Is the pheromone concentration from node i to node j at the current time t, Represent the first Only the ants are used for the production of the anti-aging agent, Is the pheromone increment from node i to node j of the kth ant on the path, m is the number of ants, Is that The pheromone concentration from node i to node j at the moment.
7. The parking lot recommendation method based on the improved ant colony algorithm according to claim 1, wherein the method is applied to a simulated traffic road network, node attributes in the traffic road network comprise distance, traffic time, traffic light quantity and parking space allowance, and the node data are randomly generated through normal distribution to construct a simulated test environment.
8. A parking lot recommendation system based on an improved ant colony algorithm, applied to the parking lot recommendation method based on an improved ant colony algorithm as set forth in any one of claims 1 to 7, comprising: The parameter initialization module is used for setting the number of ants and the pheromone matrix and determining weight parameters of the heuristic function; The path iteration optimization module is used for selecting the next node by adopting a roulette strategy according to the pheromone concentration and heuristic function values among the nodes in the current iteration until reaching the end point to form a complete path, wherein in the path construction process, part of ants execute a random walking strategy to perform global exploration; And the result output module is used for outputting the globally optimal paths in all iterations as recommended results, wherein the recommended results at least comprise target parking lots, distance and transit time information.

Description

Parking lot recommendation method based on improved ant colony algorithm Technical Field The invention relates to the technical field of heuristic algorithms and path planning, in particular to a parking lot recommendation method based on an improved ant colony algorithm. Background As urban motor vehicles continue to grow in inventory, "difficult to park" has become a common problem for drivers. To improve parking efficiency, intelligent parking lot recommendation systems based on real-time information have been developed. The core of the system is that a driving path leading to an optimal idle parking lot can be dynamically planned for a user. The path planning problem belongs to the classical combinatorial optimization problem. The ant colony algorithm is used as a heuristic optimization algorithm for simulating the foraging behavior of the ant colony in the nature, and is widely applied to the field of path planning due to the characteristics of positive feedback, distributed calculation, heuristic search and the like. The ant colony algorithm is applied to parking lot recommendation, and the basic idea is to abstract the urban road network into a graph structure, and search an optimal path by simulating the movement of 'ants' among urban nodes. However, when the conventional ant colony algorithm is directly applied to the recommended scene of the parking lot, the following obvious limitations still exist: The heuristic information is single and does not match the multi-objective requirement, and the traditional algorithm usually only uses the path distance as a main or unique factor of the heuristic information. However, in the actual parking decision, the driver needs to comprehensively balance a plurality of targets such as the driving distance, the estimated passing time, the vacancy condition of the destination parking lot and the like. The single distance index cannot accurately reflect the comprehensive cost in the real parking scene, so that the recommendation result has poor practicability. The method is easy to be in contradiction between local optimum and convergence speed, and a pheromone volatilization mechanism in an algorithm is helpful for exploring a new path, but the convergence speed is slow in a complex road network, so that the calculation cost is high. Increasing pheromone influence weights to accelerate convergence can easily lead the algorithm to fall into a local optimal solution too early, and a globally better parking lot can be missed. The exploration capability is insufficient, the global exploration capability of the path selection strategy of the traditional ant colony algorithm is limited when the complex local congestion or the instantaneous change of the parking spaces in the urban road network is dealt with, and the algorithm robustness is to be improved. Therefore, there is a lack of a method for recommending a path in a parking lot in the prior art that can efficiently and comprehensively consider multi-dimensional parking factors and achieve a good balance between fast convergence and avoiding local optima. Disclosure of Invention In order to solve the technical problems in the prior art, the invention provides a parking lot recommendation method based on an improved ant colony algorithm so as to realize a better path planning result. In order to achieve the above object, the present invention provides a parking lot recommendation method based on an improved ant colony algorithm, including: S1, setting ant quantity and an pheromone matrix, and determining weight parameters of heuristic functions; S2, in the current iteration, each ant starts from a starting point, selects the next node by adopting a roulette strategy according to the pheromone concentration among the nodes and the heuristic function value until reaching the end point, and forms a complete path, wherein in the path construction process, part of ants execute a random walking strategy to perform global exploration; S3, after all ants complete path construction, uniformly updating the pheromone matrix according to the path quality of each ant, wherein the updating process does not comprise a pheromone volatilization step; s4, repeatedly executing the steps S2 to S3 until the iteration termination condition is met, and outputting the globally optimal paths in all iterations as recommended results, wherein the recommended results at least comprise target parking lots, distance and transit time information. Preferably, the heuristic function is: ; in the formula, As a function of the heuristic properties,、、Are all the weight coefficients of the two-dimensional space model,As the distance from the i node to the j node,As the time from node i to node j,Is a nodeTo the nodeTraffic lights of (a) are counted. Preferably, the weight parameter is determined according to a recommended mode selected by a user, wherein the recommended mode includes a distance priority mode, a transit time priority mode and a compreh