参考文献/References:
[1]LIAO X T,LI Q,YANG X J,et al. Multiobjective optimization for crash safety design of vehicles using stepwise regression model[J]. Structural and Multidisciplinary Optimization,2008,35:561-569.
[2]YANG S S,TIAN Y,XIANG X S,et al. Accelerating evolutionary neural architecture search via multifidelity evaluation[J]. IEEE Transactions on Cognitive and Developmental Systems,2022,14(4):1778-1792.
[3]LI J Y,ZHAN Z Hi,ZHANG J. Evolutionary computation for expensive optimization[J]. A Survey Machine Intelligence Research,2022,19:3-23.
[4]JIN Y C,WANG H D,CHUGH T,et al. Data-driven evolutionary optimization:An overview and case studies[J]. IEEE Transactions on Evolutionary Computation,2019,23(3):442-458.
[5]WANG H D,DOHERTY J,JIN Y C,et al. Hierarchical surrogate-assisted evolutionary multi-scenario airfoil shape optimization[C]//Proceedings of the 2018 IEEE Congress on Evolutionary Computation(CEC). Rio de Janerio,Brazil:IEEE,2018.
[6]WANG H D,JIN Y C. A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems[J]. IEEE Transactions on Cybernetics,2020,50(2):536-549.
[7]CHEN G D,ZHANG K,XUE X M,et al. A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems[J]. Applied Soft Computing,2022,116:108353.
[8]MENG D B,YANG S Y,DE JESUS A M P,et al. A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower[J]. Renewable Energy,2023,203:407-420.
[9]PAN L Q,HE C,TIAN Y,et al. A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization[J]. IEEE Transactions on Evolutionary Computation,2019,23(1):74-88.
[10]LOSHCHILOV I,SCHOENAUER M,SEBAG M. A mono surrogate for multiobjective optimization[C]//Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland,USA:ACM,2010:471-478.
[11]KULKARNI V Y,SINHA P K. Pruning of random forest classifiers:A survey and future directions[C]//Proceedings of the 2012 International Conference on Data Science & Engineering(ICDSE). Cochin,India:IEEE,2012.
[12]ZHANG J Y,ZHOU A M,ZHANG G X,et al. A classification and Pareto domination based multiobjective evolutionary algorithm[C]//Proceedings of the 2015 IEEE Congress on Evolutionary Computation(CEC). Sendai,Japan:IEEE,2015.
[13]WEI F F,CHEN W N,YANG Q,et al. A classifier-assisted level-based learning swarm optimizer for expensive optimization[J]. IEEE Transactions on Evolutionary Computation,2021,25(2):219-233.
[14]TANG Z L XU L,LUO S J. Adaptive dynamic surrogate-assisted evolutionary computation for high-fidelity optimization in engineering[J]. Applied Soft Computing,2022,127:109333.
[15]WANG W Z,LIU H L,TAN K C. A surrogate-assisted differential evolution algorithm for high-dimensional expensive optimization problems[J]. IEEE Transactions on Cybernetics,2023,53(4):2685-2697.
[16]WANG H D,JIN Y C,DOHERTY J. Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems[J]. IEEE Transactions on Cybernetics,2017,47(9):2664-2677.
[17]LI J Y,ZHAN Z H,WANG H,et al. Data-driven evolutionary algorithm with perturbation-based ensemble surrogates[J]. IEEE Transactions on Cybernetics,2021,51(8):3925-3937.
[18]SUN C L,JIN Y C,CHENG R,et al. Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems[J]. IEEE Transactions on Evolutionary Computation,2017,21(4):644-660.
[19]ZHAN D W,XING H L. A fast kriging-assisted evolutionary algorithm based on incremental learning[J]. IEEE Transactions on Evolutionary Computation,2021,25(5):941-955.
[20]ZHEN H X,GONG W Y,WANG L,et al. Two-stage data-driven evolutionary optimization for high-dimensional expensive problems[J]. IEEE Transactions on Cybernetics,2023,53(4):2368-2379.
[21]CHAWLA N V,BOWYER K W,HALL L O,et al. SMOTE:synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research,2002,16(1):321-357.
[22]LI K,CHEN R Z,YAO X. A data-driven evolutionary transfer optimization for expensive problems in dynamic environments[J]. IEEE Transactions on Evolutionary Computation,2024,28(5):1396-1411.
[23]ZHU X J,GHANRAMANI Z. Learning from labeled and unlabeled data with label propagation[R]. Pittsburghers,USA:Carnegie Mellon University,2002.
[24]张俊丽,常艳丽,师文. 标签传播算法理论及其应用研究综述[J]. 计算机应用研究,2013,30(1):21-25.
[25]李正良,彭思思,王涛. 基于混合加点准则的代理模型优化设计方法[J]. 工程力学,2022,39(1):27-33.
[26]CHENG R,JIN Y. A social learning particle swarm optmization algorithm for scalable optimization[J]. Information Sciences,2015,291:43-60.
[27]ZHEN H X,GONG W Y,WANG L. Data-driven evolutionary sampling optimization for expensive problems[J]. Journal of Systems Engineering and Electronics,2021,32(2):318-330.
[28]LV Z M,WANG L Q,HAN Z Y,et al. Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization[J]. IEEE/CAA Journal of Automatica Sinica,2019,6(3):838-849.
[29]李贞. 昂贵高维多目标进化优化中代理模型的应用研究[D]. 太原:太原科技大学,2021.
[30]LIU H W,ZHOU C C,LIU F C,et al. A trust-region-like algorithm for expensive multi-objective optimization[J]. Applied Soft Computing,2023,148(C):110892.
[31]MUKESH R,LINGADURAI K,SELVAKUMARel U. Airfoil shape optimization using non-traditional optimization technique and its validation[J]. Journal of King Saud University-Engineering Sciences,2014,26(2):191-197.