Study on auto parts suppliers composition selection based on adaptive genetic algorithm

In this paper, the adaptive genetic algorithm is applied to the auto parts suppliers selection problem, through the empirical analysis, verified by the feasibility and validity of the algorithm to solve such a problem. Firstly, this paper constructs a multi-objective mathematical model for suppliers composition selection, using the linear weighting method to converse this model to a single objective model. Secondly, this paper uses the adaptive genetic algorithm to solve the mathematical model, by dynamically adjusting the crossover mutation operator to accelerate the convergence speed of the algorithm. Finally, comparison and analysis of the contents of the two aspects are shown. 1: The result of the suppliers composition selection concluded by this paper and by K car company. 2: The performance of the adaptive genetic algorithm and standard genetic algorithm. Two points can be seen from the analysis results. 1: The genetic algorithm can be used to solve the auto parts suppliers composition selection problem. 2: By adjusting the crossover and mutation operator of the genetic algorithm dynamically, the inadequacy of the genetic algorithm can be improved upon.