基于社会模型改进粒子群算法的翼型优化设计

为了解决粒子群算法在寻优过程中全局最优和局部最优的矛盾，通过在粒子群算法优化过程中引入鱼类的聚群行为，发展了一种基于社会模型的改进粒子群算法。算法以粒子可视范围的不同用法为基础，提出了两种不同的优化策略，同时探讨了种群规模等参数对算法性能的影响，并通过函数测试结果证明了两种优化策略的有效性和不同的优化特性。将改进的优化算法应用在翼型的气动优化中，显著改善了翼型气动特性，提高了算法的全局搜索能力，取得了良好的优化效果。

Airfoil Aerodynamic Optimization Design using Improved Particle Swarm Optimization Based on Social Model

In order to balance the global search ability and local search ability of Particle Swarm Optimi- zation （PSO）, a new algorithm based on the social model is proposed by introducing the collective action which belongs to Artificial Fish Swarm Algorithm （AFSA）. This new algorithm uses two optimal strategies on the basis of different visual range of particles. The effect of parameters such as population size on the algorithm is discussed. And the validity and the optimal characteristics of the two different optimal strate- gies are also testified through the function test. The proposed algorithm is applied to airfoil aerodynamic optimization and aerodynamic features of the airfoil are improved dramatically. In conclusion, the ability to search for the global minimum of the proposed algorithm optimized is much better and the optimized re- sults are quite satisfying.