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为了提高优化系统的搜索效率,发展出了社会模型这种改进智能优化算法的通用策略,在此基础上,提出了一种基于社会模型的改进粒子群优化(IPSOSM)算法。该算法对社会模型进行了分析并在此指导下,将人工鱼群算法(AFSA)中的聚群行为引入到粒子群优化(PSO)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IPSOSM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IPSOSM算法应用到翼型和机翼的气动优化设计之中,取得了良好的结果,从而表明提出的算法简洁有效,具有较好的实用性。
In order to improve the searching efficiency of the optimized system, a general strategy of improving the intelligent optimization algorithm of social model is developed. Based on this, an improved PSO algorithm based on social model is proposed. The algorithm analyzes the social model and, under the guidance of this algorithm, introduces the clustering behavior in Artificial Fish Swarm Algorithm (AFSA) into Particle Swarm Optimization (PSO) algorithm, which enriches the superior information sources between particles and enhances Particle information sharing capabilities, making IPSOSM algorithm can effectively jump out of the local optimum. Function tests show that the algorithm significantly improves the performance of PSO algorithm. The IPSOSM algorithm is applied to aerodynamic optimization design of airfoil and wing, and good results have been obtained, which shows that the proposed algorithm is concise and effective and has good practicability.