基于寄生模型的粒子群算法在气动优化中的应用

Applying a New SPPSO Algorithm to Airfoil Aerodynamic Optimization

为了解决粒子群算法(PSO)在寻优过程中全局最优和局部最优的矛盾,通过在粒子群算法中加入寄生模型,发展了一种基于寄生模型的改进粒子群算法(SPPSO)。对提出的模拟寄生算法(SP)进行了分析与验证,并将其引入到粒子群算法中,丰富了粒子之间的优势信息源,增强了粒子的信息共享能力,使得SPPSO算法能够有效地跳出局部最优。函数测试表明,该算法显著提高了PSO算法的寻优性能。将SP及SPPSO算法应用于翼型的气动优化设计中,取得了良好的效果,从而表明提出的算法准确有效,具有良好的实用性。

In order to solve the contradiction between global optimization and local optimization in the particle swarm optimization（PSO）,a new algorithm（SPPSO） combining particle swarm optimization with simulated parasitic model is presented. The simulated parasitic（SP） algorithm,after being analyzed and validated,is introduced into the particle swarm algorithm. It enriches the source of information among the particles and enhances the information sharing ability so that the SPPSO algorithm can effectively avoid local optimization. Function tests show preliminarily that,this algorithm can improve the performance of the PSO algorithm. Applying SPPSO algorithm to the airfoil aerodynamic optimization design,we achieve good results,thus showing that the proposed algorithm is effective and practical.