| EGO方法的训练算法及应用 |
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| 引用本文： | 邓枫,覃宁,伍贻兆.EGO方法的训练算法及应用[J].计算物理,2012,29(3):326-332. |
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| 作者姓名： | 邓枫 覃宁 伍贻兆 |
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| 作者单位： | 1. 南京航空航天大学航空宇航学院,江苏南京210016;Department of Mechanical Engineering,University of Sheffield,Sheffield,S1 3JD,UK
2. 南京航空航天大学航空宇航学院,江苏南京,210016 |
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| 摘 要： | 针对高效全局优化(Efficient Global Optimization,简称EGO)方法的训练问题,选择元启发式(Metaheuristic)算法、随机取样算法以及低频序列算法,并选用三个无约束、两个带约束解析优化算例以及两个气动优化算例,对这三类训练算法进行详细地比较研究,发现在元启发式算法中差分进化算法最具应用潜力,而低频序列算法可以有效降低EGO方法的随机性,其中Faure序列平均性能最优.
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| 关 键 词： | 计算流体力学 气动外形优化 克里金模型 全局优化 |
Training Algorithms for EGO Method and Applications |
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| DENG Feng
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QIN Ning
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WU Yizhao.Training Algorithms for EGO Method and Applications[J].Chinese Journal of Computational Physics,2012,29(3):326-332. |
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| Authors: | DENG Feng QIN Ning WU Yizhao |
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| Institution: | 1(1.College of Aerospace Engineering,Nanjing University of Aeronautics & Astronautics,Nanjing 210016,China; 2.Department of Mechanical Engineering,University of Sheffield,Sheffield,S1 3JD,UK) |
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| Abstract: | Three kinds of training algorithms for efficient global optimization(EGO) method are investigated.A kind of training algorithm based on low-discrepancy sequences is proposed to reduce randomness of EGO method.Performance of EGO method depends on a good training algorithm.Since training problems in EGO are non-convex and non-smooth,meta-heuristic algorithms,random algorithm and low-discrepancy sequences are chosen to address five benchmark optimization problems and two aerodynamic shape optimization problems.In these problems,differential evolution algorithm was found the best in meta-heuristic algorithms.Training algorithm based on low-discrepancy sequences can effectively reduce randomness of EGO method and Faure sequence has the best performance. |
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| Keywords: | computational fluid dynamic aerodynamic shape optimization Kriging model global optimization |
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