Application of intelligent optimization algorithm on aircraft configuration design

Aircraft configuration design is a very important part in overall aircraft design, because the general layout and many crucial parameters of the aircraft will be determined in the process. However, it is so complex and difficult that achieving an optimal solution is almost impossible with traditional optimization approaches for the nonlinear optimization of a large system. In this article, several intelligent optimization algorithms that have been developing rapidly in recent years are introduced to solve the problem of aircraft configuration design, namely, the genetic algorithm (GA), simulated annealing (SA), tabu search (TS) and Hopfield neural network (HNN). The key approaches and their operation are elaborated and their corresponding computer codes are programmed. By comparing and analyzing the results of these methods, it is shown that simulated annealing is most appropriate for solving these complicated nonlinear optimization problems. Tabu search and genetic algorithm come next, while the result of Hopfield neural network is least ideal owing to its tendency to fall easily into local optimization.