The Improvement Of Genetic Algorithm And Its Application On Optimal Method Of Urban Traffic Signal Control

Genetic algorithm is a random searching and optimizing method which simulates natural descendiblity mechanism and biology evolution theory. This method has some advantages that other usual methods don't have because of its twocharacters——implicit parallelism and global searching. But after all, geneticalgorithm is a newborn optimizing method and both its theory and its realization need to be improved. Only in this way, can genetic algorithm apply to the practice more effectively and widely.After basic theory , basic factors and theory base of genetic algorithm being introduced detailedly, aiming at limitations on application of the simple genetic algorithm, this thesis bring forwards some improving measures: (1) From genetic algorithm itself, the niche technique is adopted and it combines elitists reservation model, scheme of keeping population diversiform, mode of demarcating fitness, improved adaptive crossover and mutation rate to improve basic genetic algorithm. (2) During the period of searching of genetic algorithm, blending steepest descent method whose ability of local searching is strong, forming hybrid genetic algorithm to enhance efficiency of circulating and quality of computing.With the development of our national economy and the improving of the civilian living standard level, the car has been popular for every one. The problem of urban traffic is increasingly serious. So it is urgent for all of us to solve this problem by adopting modern scientific technology and intelligent method. In order to mitigate city traffic jam, the traffic flow characteristics of urban intersections were analyzed. firstly, a nonlinear function model of urban single—point intersections was established, in which shortest average delay of vehicles were taken as objectives, and phase effective green time, saturation degree were taken as restrictions, the objective function of the model was solved by hybrid genetic algorithm. Solved result indicates that obtaining perfect effect, which manifests the advantage of hybrid genetic algorithm. Secondly, aiming at crowd status of intersection, an urban intersection controlling model was established, in which shortest total queue length of vehicles were taken as objectives, phase effective green time and cycle time length were taken as controlling variables, the objective function of the model was solved by improved genetic algorithm. The result indicates that controlling methods of the paper can make delayed vehicles of intersection fewer than before. At the same time, it incarnates that improved genetic algorithm is more excellent than basic genetic algorithm on stability, optimization and convergence speed of results.