Self-adaptive Intergeneration Projection Genetic Algorithm And Its Application In Vehicle Suspension's Optimization Parameters

With the rapid development of automobile industry, suspension which is a significant part of the vehicle has been attached increasingly greater importance than ever before. The performance of passive suspension applied widely can be improved by optimizing its structure. As an optimized design method, genetic algorithm has been improved and developed constantly. The paper proposed self-adaptive intergeneration projection genetic algorithm to improve the optimizing efficiency and used it to optimize the structural parameters of suspension.The following research has been done in the paper.(1) Considering the shortage and limitation of genetic algorithm, a new method is proposed which combines the self-adaptive strategy and intergeneration projection genetic algorithm, so the crossover probability and mutation probability can be varied according to the current individual fitness in the population so that the algorithm searching speed and convergence performance are both improved. The intergeneration projection genetic algorithm is compared by using test function and the improvement method is validated(2) The dynamic characteristics of the a quarter passive suspension model with two freedoms has been analyzed. After its simulation model is established in MATLAB, the model's structural parameters are optimized by using self-adaptive intergeneration projection genetic algorithm. The curves of vehicle acceleration, body displacement and suspension displacement acquired by two algorithms are compared, the advantages of the self-adaptive intergeneration projection genetic algorithm is proved once more and the suspension performance and the driving characteristic are both improved.(3) The simulation model of wishbone type independent front suspension is created in ADAMS according to its real characteristics. Three parameters which have the greatest influence on the suspension performance are selected as the design variants in the optimization work. The binomial simulation model of the wishbone type independent front suspension is established by RSM (Respond Surface Method). Then these three parameters are optimized by self-adaptive intergeneration projection genetic algorithm to decrease the tire wear.