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| 基于改进粒子群算法的翼型多目标优化研究 | |
| 引用本文： | 王荣伟,高正红. 基于改进粒子群算法的翼型多目标优化研究[J]. 应用力学学报, 2011, 28(3) |
| 作者姓名： | 王荣伟 高正红 |
| 作者单位： | 西北工业大学,710072西安 |
| 摘 要： | 在多目标优化研究中,为改善多目标粒子群算法的局部搜索能力,以标准粒子群算法为基础,引入单点模拟退火算法,局部进化最优个体,采用基于目标向量的共享函数法评价适应值.标准测试函数优化实例表明:本文算法比标准粒子群算法具有更好的收敛稳定性和收敛速度,收敛速度提高了近50%;针对某翼型的气动优化设计结果表明:改进算法有效缩短了优化时间,迭代代数由61减为49,调用CFD由4880减为4250次;阻力系数、升力系数、低头力矩系数分别改进了9.23%、0.42%、16.4%,取得了较好的优化效果. |
| 关 键 词： | 多目标粒子群 模拟退火 共享函数 气动优化 |
Improved multi-objective particle swarm optimization algorithm for aerofoil aerodynamic optimization design |
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| Wang Rongwei Gao Zhenghong. Improved multi-objective particle swarm optimization algorithm for aerofoil aerodynamic optimization design[J]. Chinese Journal of Applied Mechanics, 2011, 28(3) | |
| Authors: | Wang Rongwei Gao Zhenghong |
| Affiliation: | Wang Rongwei Gao Zhenghong(Northwestern Polytechnical University,710072,Xi'an,China) |
| Abstract: | Based on Multi-Objective Particle Swarm Optimization(MOPSO),simulated annealing algorithm is introduced into MOPSO to evaluate the best individual in the local.Share function(SF) based on object vector is used to judge the fitness.Function optimization results indicate that the algorithm possess a better convergence stability and convergence speed.Research in this paper indicates that the convergence speed of the algorithm(SF-MOPSO) enhanced fifty percents.Then these algorithms are applied on an airfoil sha... |
| Keywords: | multi-objective particle swarm optimization simulated annealing algorithm share function aerodynamic optimization. |
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