基于改进多目标布谷鸟搜索算法的翼型气动优化设计

doi:10.7527/S1000-6893.2018.22550

流体力学与飞行力学

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基于改进多目标布谷鸟搜索算法的翼型气动优化设计

张鑫帅, 刘俊, 罗世彬

中南大学航空航天学院, 长沙 410083

收稿日期:2018-07-17 修回日期:2018-10-13 出版日期:2019-06-15 发布日期:2018-12-06
通讯作者: 刘俊 E-mail:liu.jun@csu.edu.cn
基金资助:
国家自然科学基金（11702332，11272349）

An improved multi-objective cuckoo search algorithm for airfoil aerodynamic optimization design

ZHANG Xinshuai, LIU Jun, LUO Shibin

School of Aeronautics and Astronautics, Central South University, Changsha 410083, China

Received:2018-07-17 Revised:2018-10-13 Online:2019-06-15 Published:2018-12-06
Supported by:
National Natural Science Foundation of China (11702332,11272349)

摘要/Abstract

摘要： 布谷鸟搜索（CS）算法是一种新型的受自然现象启发的元启发式智能优化算法，其强大的全局搜索能力和收敛速度受到了广泛关注。多目标布谷鸟搜索（MOCS）算法是一种在单目标布谷鸟算法基础上发展的可以直接求解Pareto解集的多目标优化算法。针对原始MOCS算法的不足，采用一系列措施以提高算法的收敛精度、收敛速度以及解的均匀性：通过引入非支配排序与拥挤距离来改进解的适应度评估；通过改进随机游走策略来提高局部搜索能力；通过引入改进的自适应丢弃概率策略来提高算法的收敛速度；加入档案管理机制，提高解的均匀性。典型的多目标数值算例结果表明，改进的MOCS算法相较于当前主流的NSGA-Ⅱ算法拥有更快的收敛速度和更高的收敛精度。以RAE2822双目标升阻比优化设计为例，将改进的MOCS算法应用于多目标气动优化中，改进的MOCS算法共获得64个Pareto解，优化后的翼型气动性能有明显的提升，设计者可以根据自己的偏好选取不同的Pareto解。对于气动优化问题，改进的MOCS算法与目前主流的NSGA-Ⅱ相比，收敛速度更快。

关键词: 多目标布谷鸟搜索算法, Pareto最优解集, 多目标优化, 翼型, 气动优化设计

Abstract: Cuckoo Search (CS) algorithm is a newly proposed meta-heuristic optimization algorithm inspired by natural phenomena. It received wide attention due to its powerful global searching capability and fast convergence speed. The Multi-Objective Cuckoo Search (MOCS) algorithm is a multi-objective optimization algorithm developed on the basis of the single-objective cuckoo search which can directly obtain a set of Pareto solutions. Aiming at alleviating the shortcomings of the original MOCS algorithm, a series of methodologies are introduced to improve the convergence accuracy, convergence speed, and distribution of the solutions:the fast non-dominated sorting and crowding distance are introduced to improve the fitness evaluation of solutions, a random walk strategy is modified to improve local search ability, an adaptive abandon probability strategy is used to improve the convergence speed, and an archive management mechanism is added to improve the uniformity of the distribution of the Pareto set. The results of the benchmark analytical multi-objective tests show that the improved MOCS algorithm has a faster convergence speed and higher convergence accuracy than the original MOCS as well as the NSGA-Ⅱ algorithm. Finally, taking the RAE2822 two-point lift-to-drag ratio maximization design as an example, the improved MOCS algorithm is applied to a multi-objective aerodynamic optimization problem. The results show that the improved MOCS algorithm can obtain 64 Pareto solutions. The aerodynamic performances of the optimized airfoils are significantly improved, and the designers can choose different Pareto solutions based on their own requirements. For the aerodynamic optimization problem, the convergence speed of improved MOCS algorithm is faster than MOCS and NSGA-Ⅱ algorithms.

Key words: multi-objective cuckoo search algorithm, Pareto optimal set, multi-objective optimization, airfoil, aerodynamic optimization design

中图分类号:

V211.4

张鑫帅, 刘俊, 罗世彬. 基于改进多目标布谷鸟搜索算法的翼型气动优化设计[J]. 航空学报, 2019, 40(6): 122550-122550.

ZHANG Xinshuai, LIU Jun, LUO Shibin. An improved multi-objective cuckoo search algorithm for airfoil aerodynamic optimization design[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(6): 122550-122550.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于改进多目标布谷鸟搜索算法的翼型气动优化设计

An improved multi-objective cuckoo search algorithm for airfoil aerodynamic optimization design

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