个人简介

华中科技大学机械科学与工程学院教授、博士生导师。近年来主持国家自然科学基金项目3项，装备预先研究项目2项，数控重大专项2项，参与973项目1项，发表论文100余篇，其中SCI收录50余篇，发明专利3项，软件著作版权2项，出版专著2部。 开设课程 本科生课程（质量控制、可靠性工程） 科研项目： 1、国家自然科学基金项目“基于稳健近似和适应性求解的复杂机械产品可靠性设计优化方法研究”，2017-2020，主持 2、装备预研项目“智能制造单元技术”，2019-2021，主持 3、企业合作项目“3C热弯装备模具传热过程模拟实验及其工艺优化”，2019-2020，主持 4、973项目“高服役性能海洋动力定位装备制造的基础研究”子课题5“全回转推进装备响应灵敏性分析与敏感装配参数优化”，2014-2019，排名第2。 5、国家自然科学基金项目“基于目标级联分析和变可信度近似的复杂机械产品多学科设计优化研究”，2012-2015，主持。 6、数控重大专项“超重型数控单柱移动立式铣车床”，2013-2016，主持。 7、数控重大专项“核电蒸汽发生器孔板加工数控龙门移动式多主轴钻床”，2013-2016，主持。 8、装备预研子课题“柔性线缆三维立体成型与组装”，2012-2015，主持。 书籍 [1] 高亮、邱浩波、肖蜜等，优化驱动的设计方法，清华大学出版社，2020年 [2] 饶运清、邱浩波、曾文会等，全回转推进器装配性能分析与优化，科学出版社，2019年。

研究领域

基于近似模型的优化设计 可靠性设计优化 工艺智能优化方法 设备健康管理和系统可靠性建模

从事复杂机械产品结构工艺性能优化设计与可靠性建模优化方面的研究和应用工作，致力于通过数学、人工智能和力学手段使产品性能和可靠性得到精准预测和优化

近期论文

查看导师最新文章 （温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

[1] Chen Jiang, Haobo Qiu*, Liang Gao, Liming Chen. Real-time estimation error-guided active learning Kriging method for time-dependent reliability analysis. Applied Mathematical Modelling. 2020, 77(1): 82-98. 高被引。 [2] Zan Yang, Haobo Qiu*, Liang Gao, Chen Jiang, Liming Chen. Surrogate-assisted classification-collaboration differential evolution for expensive constrained optimization problems. Information Sciences. 2020, 508(1): 50-63. [3] Chen Jiang, Haobo Qiu*, Xiaoke Li, Zhenzhong Chen, Liang Gao, Peigen Li. Iterative reliable design space approach for efficient reliability-based design optimization. Engineering with Computers. 2020, 36: 151–169. [4] Liming Chen, Haobo Qiu*, Liang Gao, Chen Jiang, Zan Yang. Optimization of expensive black-box problems via Gradient-enhanced Kriging. Computer Methods in Applied Mechanics and Engineering. 2020, 362(4): 112861. [5] Chen Jiang, Haobo Qiu*, Liang Gao, Dapeng Wang, Zan Yang, Liming Chen. EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces. Reliability Engineering &System Safety. 2020, 198(6): 106906. [6] Dapeng Wang, Chen Jiang, Haobo Qiu*, Jinhao Zhang, Liang Gao. Time-dependent reliability analysis through projection outline-based adaptive Kriging. Structural and Multidisciplinary Optimization. 2020, 61:1453–1472. [7] Liming Chen, Haobo Qiu*, Liang Gao, Chen Jiang, Zan Yang. A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems. Applied Mathematical Modelling. 2019, 69(5): Pages 15-31. [8] Chen Jiang, Haobo Qiu*, Zan Yang, Liming Chen, Liang Gao, Peigen Li. A general failure-pursuing sampling framework for surrogate-based reliability analysis. Reliability Engineering and System Safety. 2019, 183(3): 47-59. 高被引 [9] Zan Yang, Haobo Qiu*, Liang Gao, Xiwen Cai, Chen Jiang, Liming Chen. A surrogate-assisted particle swarm optimization algorithm based on efficient global optimization for expensive black-box problems. Engineering Optimization. 2019, 51(4): 549-566. [10] Chen Jiang, Dapeng Wang, Haobo Qiu*, Liang Gao, Liming Chen, ZanYang. An active failure-pursuing Kriging modeling method for time-dependent reliability analysis. Mechanical Systems and Signal Processing, 2019, 129(8): 112-129 [11] Zan Yang, Haobo Qiu*, Liang Gao, Chen Jiang, Jinhao Zhang. Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems. Journal of Global Optimization. 2019, 74, 327–359. [12] Xiwen Cai, Haobo Qiu, Liang Gao et al. An efficient surrogate-assisted particle swarm optimization algorithm for high-dimensional expensive problems. July 2019. Knowledge-Based Systems. [13] Xiwen Cai, Liang Gao, Xinyu Li, Haobo Qiu. Surrogate-guided differential evolution algorithm for high dimensional expensive problems. May 2019. Swarm and Evolutionary Computation. [14] Liming Chen, Haobo Qiu*, Chen Jiang, Mi Xiao, Liang Gao. Support Vector enhanced Kriging for metamodeling with noisy data. Structural and Multidisciplinary Optimization. 2018, 57(4): 1611-1623. [15] Liming Chen, Haobo Qiu*, Chen Jiang, Xiwen Cai, Liang Gao. Ensemble of surrogates with hybrid method using global and local measures for engineering design. Structural and Multidisciplinary Optimization. 2018, 57 (4): 1711-1729. [16] Cai Xiwen, Qiu Haobo*, Gao Liang, Li xiaoke, Shao Xinyu. A hybrid global optimization method based on multiple metamodels. Engineering Computations. 2018, 35(6): 71- 90. [17] C Jiang, X Cai, H Qiu*, L Gao, P Li. A two-stage support vector regression assisted sequential sampling approach for global metamodeling. Structural & Multidisciplinary Optimization, 2018, 58(4): 1657–1672. [18] Zhenzhong Chen, Xiaoke Li, Ge Chen, Liang Gao, Haobo Qiu, Shengze Wang. A probabilistic feasible region approach for reliability-based design optimization. Structural and Multidisciplinary Optimization, January 2018, Volume 57, Issue 1, pp 359–372. [19] Zhenzhong Chen, Zihao Wu, Xiaoke Li, etal. Haobo Qiu. An accuracy analysis method for first-order reliability method. November 2018. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science. [20] Jinhao Zhang, Mi Xiao, Liang Gao, Haobo Qiu, Zan Yang. An improved two-stage framework of evidence-based design optimization. April 2018, Structural and Multidisciplinary Optimization 58(1).