Research on Aircraft Assembly Optimization Based on Genetic Algorithms

To procure the optimal assembly sequence, enhance the efficiency of aircraft assembly, and expand the diversity of assembly sequences, this study employs an improved genetic algorithm. It introduces an aircraft component assembly interference matrix into the genetic algorithm, refining it to accelerate the convergence rate and reduce the computational cost of mutation. The fuzzy set theory is utilized to determine the fitness function for the optimization of aircraft component assembly sequences, thus achieving sequence optimization. This serves as the evaluation criterion for aircraft component assembly sequence optimization, alongside the design of corresponding constraints. Based on this evaluation criterion and these constraints, an objective function for assembly sequence optimization is established. The proposed method then solves this objective function using the improved genetic algorithm. Experiments have shown that this approach effectively optimizes assembly sequences, increasing assembly efficiency. Notably, under the same population size, this method yields a significantly higher number of feasible assembly sequences, indicating superior sequence diversity and a faster convergence rate, thus underpinning its effectiveness and efficiency in aircraft assembly processes.