摘要
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。
A universal strategy named the social model is developed and a new algorithm named improved particle swarm optimization based on social model （IPSOSM） algorithm is proposed in order to improve the searching efficiency of an opti- mization system. The social model is first analyzed and, with the guidance of the social model theory, the collective action which belongs to artificial fish swarm algorithm （AFSA） is then introduced into particle swarm optimization （PSO） algorithm. This enlarges the best information of particle swarm and improves the ability of communication among the particle swarm, which helps get rid of strapping into a local minimum. Function test results show that the IPSOSM algorithm has much better optimazing ability than the PSO algorithm. The IPSOSM algorithm is applied to an airfoil aerodynamic design and a wing aer- odynamic design and satisfactory optimal results are obtained, which proves the simplicity and efficiency of the social model.
出处
《航空学报》
EI
CAS
CSCD
北大核心
2012年第10期1809-1816,共8页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金(11172242)~~
关键词
粒子群优化
人工鱼群算法
社会模型
搜索效率
气动优化设计
particle swarm optimization
artificial fish swarm algorithm
social model
searching efficiency
aerodynamic optimization design
