Application of Improved Genetic Algorithm in Aircraft Industry Process Simulation

In this study, aiming at the optimization problem of the production line of discrete aviation manufacturing enterprises, using traditional genetic algorithm to optimize and improve it has the disadvantages of slow convergence, easy to fall into local extremes, and low search efficiency. By improving the crossover probability and mutation probability according to the adaptability of the group, to ensure that the diversity of the understanding of the group is not compromised, so as to better generate new individuals, get rid of the local extreme value, search for the global optimal solution, and adopt the optimal strategy to ensure the convergence of the improved adaptive genetic algorithm. Taking a production line of an aerospace manufacturing company as an example, an improved adaptive genetic algorithm was adopted for complex production line models to obtain an optimal resource matching solution, which provides a new way of thinking for improving the production capacity and efficiency of the enterprise.