Research And Application Of Particle Swarm Optimization Algorithm Using In Parking Space Inducing On Large Parking Lot

In recent years, private car ownership in China increasing rapidly, but thecorresponding parking facilities construction and the use have been unable to meet theneeds of rapid increase of private cars. Parking has become a problem in every carowner. Survey found that the existing parking facilities utilization rate is not high alsois a factor of the problem, in order to save parking the car time, should follow theexample of some developed countries, build parking guidance system within parkinglot.This paper hope through the study of parking lot parking guidance system, toachieve two purposes. First of all, by parking Spaces to the vehicle driver providedreal-time status information, they can in the shortest possible time to find the rightspace, thus avoiding queue congestion phenomenon appeared at the entrance to theparking lot, causing traffic congestion because too long lines will no longer appear.Secondly, improve the utilization rate of existing parking facilities, the system cancoordinate urban parking guidance system, there will be parking demand vehiclesassigned to the appropriate parking facilities, achieve the goal of full and reasonableuse of public parking facilities.Particle swarm optimization (PSO) algorithm is an optimization algorithm basedon bionics is proposed, the basic concept of particle swarm optimization (PSO)algorithm from the observation and study of birds foraging behavior. Suppose a flockrandomly in one area and search for food source, and the food source in this area isthe only, and the position of the food source for all individuals in the flock areunknown, but every individual knows your current and the distance between the food source, as well as the current position, each individual in a population in such a case,the search for the most simple and effective strategy to the food source is around withfood source distance is the shortest of the individual to search.Particle swarm algorithm is real-time, highly effective, does not depend on thespecific characteristic of the problem, but it also has many limitations, aimed at thelimitations of the particle swarm optimization (PSO) algorithm, the crossover andchoice of differential algorithm is introduced into the particle swarm algorithm of DE-PSO algorithm, which can overcome the particle swarm algorithm easy to fall intolocal optimum itself shortcoming. Will crossover and mutation of genetic algorithm iscombined with particle swarm optimization (PSO) algorithm, makes the particleswarm algorithm can maintain the diversity of population and avoid the prematureconvergence. Also introduced to dynamically adjust inertia weight, according to thediversity of particle degree adjust inertia weight, the particle swarm algorithm morehasten is mature.Essence view, parking induction process itself is a specific path planningproblem of neural network can effectively help the path optimization problem toconstruct an appropriate fitness function, the fact proved that the particle swarmalgorithm can be fast and efficient to solve the path planning problem.