Abstract: Most existing optimization methods of airplane primary parameters are insufficient in efficiency and adaptability. This article presents a new nonnumerical optimization method that uses the particle swarm algorithm to improve the optimization performance of airplane primary parameters, because this method is based on the evolvement technology of colony intelligence and has the advantages of facility and availability to multiobjective optimization problems with complex restrictions. The application approach of this algorithm in airplane primary parameters is explained in detail, including ① construction of the particle swarm algorithm in the optimization of airplane primary parameters selecting range, payload, take-off and landing distances as the goals of optimization; ②self-adaptive method of choice coefficients; a new way to calculate the coefficients automatically in different steps; and③flow chart of optimization of airplane primary parameters. Experimental data of a medium-range aerotransport are presented. Compared with the reference, the data show good coherence and rationality. Thus the proposed method can be effectively applied to airplane design.

Key words: multiobjective optimization, airplane design, airplane primary parameters, particle swarm algorithm