Optimal Design Flexible Multi-body Vehicle Suspensions Parameters Based on Improved Genetic Algorithm

摘要

Genetic algorithm is a random search method, which has very good ability for global searching. It is one of the optimal methods that is the most affecting and widely used currently. However, some deficiencies still exist in genetic algorithms, for example, subjectivity problem in probability parameters selection and prematurely problem more or less. Now, there are lots of improvements are presented against the deficiencies ,such as adjustment population scale, optimization controlled variable and so on .In this paper an improved genetic algorithm base on gray encoding has been given. Vehicle suspension system is a typical mutli-boby systems of precision space. And its performance is directly related to many vehicle capabilities, such as smoothly, the manipulation of stability, security and braking. It is greatly significant for the development of national vehicle industry to choose appropriate methods for optimization design of the vehicle suspension dynamics system parameters. Currently, the determinacy optimization methods are used to optimize the problems generally. This paper focuses on optimization design of flexible multi-body vehicle suspensions parameters through above improved genetic algorithm, the result shows that the calculation quality and efficiency have greatly been improved compared with other methods currently.