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论文:2022,Vol:40,Issue(3):493-503 |
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引用本文： |
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王培君, 夏露, 周文硕, 栾伟达. 基于并行交换的增强粒子群优化算法在气动优化中的应用[J]. 西北工业大学学报 |
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WANG Peijun, XIA Lu, ZHOU Wenshuo, LUAN Weida. Applying to aerodynamic optimization an enhanced particle swarm optimization algorithm based on parallel exchange[J]. Northwestern polytechnical university |
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| 基于并行交换的增强粒子群优化算法在气动优化中的应用 |
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| 王培君, 夏露, 周文硕, 栾伟达 |
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| 西北工业大学 航空学院, 陕西 西安 710072 |
| 摘要: |
| 粒子群优化(PSO)算法易于实现,对优化问题可以获得质量较高的解,被广泛应用在如气动优化这种非线性高难度问题中,但是面对多峰问题容易陷入局部最优,存在鲁棒性较差的问题,为了提高PSO的鲁棒性,提出了基于并行交换的增强粒子群优化算法(EPSOBPE)。该算法通过布谷鸟搜索算法(CSA)和PSO种群并行进化,分层交换操作和增强学习策略来增强算法寻优能力与鲁棒性。该算法兼具了CSA的全局搜索能力和PSO的局部能力,使得新算法具有极强的鲁棒性。函数测试表明,新算法相较于其他智能优化算法有更强的鲁棒性,对不同问题的适应能力更强。将EPSOBPE算法应用到RAE2822翼型和M6机翼的气动优化设计中,相较于其他算法可以得到更好的效果,从而表明新算法有鲁棒性,同时兼具了更好的寻优能力。 |
| 关键词:
粒子群优化算法
布谷鸟搜索算法
气动优化设计
全局优化
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| Applying to aerodynamic optimization an enhanced particle swarm optimization algorithm based on parallel exchange |
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| WANG Peijun, XIA Lu, ZHOU Wenshuo, LUAN Weida |
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| School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| The particle swarm optimization (PSO) algorithm is easy to implement and can obtain high-quality solutions to optimization problems. It is widely applied to nonlinear and difficult problems such as aerodynamic optimization. However, to solve multi-modal problems, it easily falls into locally optimal values, showing that its robustness is poor. In order to improve the robustness of the PSO algorithm, an enhanced particle swarm optimization algorithm based on parallel exchange (EPSOBPE) is proposed. The algorithm enhances the optimization capability and its robustness through the parallel evolution of the cuckoo search algorithm (CSA), PSO population, hierarchical exchange operation and reinforcement learning strategy. Therefore, the algorithm has both the global search capability of the CSA and the local capability of the PSO algorithm, thus making the EPSOBPE very robust. Functional test results show that the EPSOBPE has stronger robustness and adaptability to different problems than other intelligent optimization algorithms. Moreover, the EPSOBPE is applied to the aerodynamic optimization design of the RAE2822 airfoil and the M6 wing. Compared with other algorithms, the EPSOBPE is more robust, and its optimization capability is better. |
| Key words:
particle swarm optimization algorithm
cuckoo search algorithm
aerodynamic optimization design
global optimization
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| 收稿日期: 2021-06-18
修回日期:
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| DOI: 10.1051/jnwpu/20224030493 |
| 基金项目: 翼型、叶栅空气动力学重点实验室基金(6142201200106)资助 |
| 通讯作者: 夏露(1977—),女,西北工业大学副教授,主要从事飞行器设计研究。e-mail:xialu@nwpu.edu.cn
Email：xialu@nwpu.edu.cn |
| 作者简介: 王培君(1998—),西北工业大学硕士研究生,主要从事飞行器设计研究。
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参考文献: |
|
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[1] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of International Conference on Neural Networks, 1995: 1942-1948
[2] YANG X S, DEB S. Cuckoo search via Lévy flights[C]//2009 World Congress on Nature & Biologically Inspired Computing, 2009: 210-214
[3] STORN R, PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359
[4] CLERC M, KENNEDY J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58-73
[5] MENDES R, KENNEDY J, NEVES J. The fully informed particle swarm: simpler, maybe better[J]. IEEE Trans on Evolutionary Computation, 2004, 8(3): 204-210
[6] 李丁, 夏露. 一种混合粒子群优化算法在翼型设计中的应用[J]. 航空计算技术, 2010, 40(6): 66-71 LI Ding, XIA Lu. Application of a hybrid particle swarm optimization to airfoil design[J]. Aeronautical Computing Technique, 2010,40(6): 66-71 (in Chinese)
[7] 石季英, 巩莹, 薛飞, 等. 计及电动汽车的PSO-CS算法配网重构[J]. 电力系统及其自动化学报, 2018, 30(2): 66-70 SHI Jiying, GONG Ying, XUE Fei, et al. Distribution network reconfiguration with PSO-CS algorithm considering electric vehicles[J]. Proceedings of the CSU-EPSA, 2018,30(2): 66-70 (in Chinese)
[8] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61
[9] 胡建秀, 曾建潮. 微粒群算法中惯性权重的调整策略[J]. 计算机工程, 2007(11): 193-195 HU Jianxiu, ZENG Jianchao. Selection on intertia weicht of particle swarm optimization[J].Computer Engineering, 2007(11): 193-195 (in Chinese)
[10] COOK P, MCDONALD M, FIRMIN M. Aerofoil RAE-2822: pressure distributions, and boundary layer and wake measurements[R]. AGARD AR-138-A6, 1979
[11] KULFAN B M. Universal parametric geometry representation method[J]. Journal of aircraft, 2008, 45(1): 142-158
[12] 卜月鹏, 宋文萍, 韩忠华, 等. 基于CST参数化方法的翼型气动优化设计[J]. 西北工业大学学报,2013, 31(5): 829-836 BU Yuepeng, SONG Wenping, HAN Zhonghua, et al. Aerodynamin optimization design of airfoil based on CST parameterization method[J]. Journal of Northwestern Polytechnical University, 2013, 31(5): 829-836 (in Chinese)
[13] SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989, 4(4): 409-423
[14] 王超, 高正红, 张伟, 等.自适应设计空间扩展的高效代理模型气动优化设计方法[J]. 航空学报, 2018, 39(7): 45-63 WANG Chao, GAO Zhenghong, ZHANG Wei, et al. Effivient surrogate-based aerodynamic design optimization method with adaptive design space expansion[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(7): 45-63 (in Chinese)
[15] SCHMITT V, CHARPIN F. Pressure distributions on the ONERA-M6-Wing at transonic mach numbers[R]. AGARD AR-138-B1, 1979
[16] SEDERBERG T W, PARRY S R. Free-form deformation of solid geometric models[C]//Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, 1986: 151-160 |
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1．夏露, 张阳, 孙腾腾.基于寄生模型的粒子群算法在气动优化中的应用[J]. 西北工业大学学报, 2015,33(2): 178-184 |
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