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论文:2022,Vol:40,Issue(6):1385-1393 |
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引用本文： |
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樊华羽, 李典, 郝海兵, 梁益华. 基于Alpha-stable的粒子群算法变异策略研究及气动优化应用[J]. 西北工业大学学报 |
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FAN Huayu, LI Dian, HAO Haibing, LIANG Yihua. A fast PSO algorithm based on Alpha-stable mutation and its application in aerodynamic optimization[J]. Journal of Northwestern Polytechnical University |
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| 基于Alpha-stable的粒子群算法变异策略研究及气动优化应用 |
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| 樊华羽, 李典, 郝海兵, 梁益华 |
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| 中国航空工业集团西安航空计算技术研究所, 陕西 西安 710065 |
| 摘要: |
| 提出了一种基于Alpha stable分布的新型变异方法。针对粒子群算法容易陷入局部最优的缺点,通过对比分析确定了一种调整Alpha stable分布的稳态系数动态变异策略,使粒子群算法能够在搜索初始阶段具有更强的种群多样性以及算法探索能力,减少陷入局部最优的可能;在算法末期增强粒子群优化算法的局部搜索能力,提高解的精度。将基于Alpha stable变异的粒子群优化算法(Alpha stable particle swarm optimization,ASPSO)与多种改进型粒子群优化算法以及差分进化算法(differential evolution algorithm,DE)进行了比较,基准测试函数结果表明新建立的ASPSO算法极大地提高了算法的收敛速度和精度。将其应用到RAE2822翼型的单点跨声速减阻优化中,在保持种群规模等参数相同的情形下,ASPSO算法的优化效果和效率都远高于传统PSO算法,最终得到的翼型也比PSO优化的翼型具有更高的升阻比,翼面波阻有明显降低。 |
| 关键词:
粒子群优化算法
Alpha-stable分布
动态变异
气动优化
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| A fast PSO algorithm based on Alpha-stable mutation and its application in aerodynamic optimization |
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| FAN Huayu, LI Dian, HAO Haibing, LIANG Yihua |
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| AVIC Xi'an Aeronautics Computing Technique Research Institute, Xi'an 710065, China |
| Abstract: |
| In this paper, a novel mutation method based on Alpha stable distribution is proposed. Aiming at the shortcoming that the particle swarm optimization (PSO) is easy to fall into local optimum, a dynamic mutation strategy of Alpha stable distribution is determined through comparative analysis. This mutation strategy can make the particle swarm optimization algorithm based on Alpha-stable (ASPSO) have stronger population diversity and exploration ability in the initial stage of search, and make the algorithm avoid falling into local optimum. At the end of the algorithm, it can also enhance the local search ability of the particle swarm optimization algorithm and improve the accuracy of the solution. The ASPSO algorithm is compared with several improved particle swarm optimization algorithms and differential evolution algorithm. The benchmark function results show that the new ASPSO algorithm greatly improves the convergence speed and accuracy of the algorithm. Finally, both PSO and ASPSO algorithms were applied to a minimal drag optimization design of the RAE2822 airfoil and compared. The comparisons show that the ASPSO algorithm achieves a lower drag in a faster rate which lifts and pitching moment is well constrained. |
| Key words:
particle swarm optimization
Alpha-stable distribution
dynamic mutation
aerodynamic optimization
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| 收稿日期: 2022-03-06
修回日期:
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| DOI: 10.1051/jnwpu/20224061385 |
| 通讯作者:
Email： |
| 作者简介: 樊华羽(1985—),中国航空工业集团西安航空计算技术研究所工程师,主要从事飞行器总体设计、飞行器气动与隐身多学科优化研究。e-mail:kevin.fan@163.com
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