王凤梅,何小娟,孙超利,狄亚坤

• 1:太原科技大学应用科学学院
• 2:太原科技大学计算机科学与技术学院

摘要(Abstract)：

针对EGO (Efficient Global Optimization)算法在求解昂贵优化问题中需要大量真实评价获得最优解的问题，提出一种基于并行填充准则的EGO算法。首先，设置了离群度量因子，提高分布稀疏区域样本点被选择的几率，从而提高算法优化效率；其次，引入了影响函数，依据已选择填充点对后续待选填充点的影响，构造新的EI(Expected Improvement,简称EI)函数依次选择多个填充点，并对这些点并行计算，从而减少了计算成本。在14个测试函数上对所提算法进行仿真实验，与其它典型代理模型辅助的优化算法进行测试对比，实验结果表明所提算法在有限的的评价次数下拥有更快的收敛速度。

关键词(KeyWords)： EGO优化算法;Kriging代理模型;期望增量;并行计算

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(61876123);; 山西省重点研发计划项目(2021020201010002)

作者(Author): 王凤梅,何小娟,孙超利,狄亚坤

参考文献(References)：

• [1] 沈浩杰，夏杨，陈刚.某型无人机机翼预埋骨架拓扑优化设计[J].计算机仿真，2022,39(3):79-84.
• [2] XIONG Z,GUO K,CAI H,et al.Distillation process optimization:A screening-clustering assisted kriging optimization method[J].Chemical Engineering Science,2021,238:116597.
• [3] LI Y,SHI J,CEN H,et al.A kriging-based adaptive global optimization method with generalized expected improvement and its application in numerical simulation and crop evapotranspiration[J].Agricultural Water Management,2021,245:106623.
• [4] 金希东.遗传算法及其应用[D].成都：西南交通大学，1996.
• [5] 于成龙，付国霞，孙超利，等.全局与局部模型交替辅助的差分进化算法[J].计算机工程，2022,48(3):115-123.
• [6] POLI R,KENNEDY J,BLACKWELL T.Particle swarm optimization[J].Swarm intelligence,2007,1(1):33-57.
• [7] 周陈龙，盛誉，路昕.基于试验数据的复杂系统性能代理模型及应用[J].科技通报，2022,38(3):7-13+41.
• [8] SUN C,JIN Y,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.
• [9] 朱苗苗，潘伟杰，刘翔，等.基于BP神经网络代理模型的交互式遗传算法[J].计算机工程与应用，2020,56(2):146-151.
• [10] HUANG G B,ZHOU H,DING X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2011,42(2):513-529.
• [11] 田杰，孙超利，谭瑛，等.基于多点加点准则的代理模型辅助社会学习微粒群算法[J].控制与决策，2020,35(1):131-138.
• [12] JONES D R,SCHONLAU M,WELCH W J.Efficient global optimization of expensive black-box functions[J].Journal of Global optimization,1998,13(4):455-492.
• [13] ADAM D BULL.Convergence Rates of Efficient Global Optimization Algorithms.[J].Journal of Machine Learning Research,2011,12(10):3-4.
• [14] JEONG S,OBAYASHI S.Efficient global optimization (EGO)for multi-objective problem and data mining[J].2005 IEEE congress on evolutionary computation,IEEE,2005,3:2138-2145.
• [15] SóBESTER A,LEARY S J,KEANE A J.A parallel updating scheme for approximating and optimizing high fidelity computer simulations[J].Structural and multidisciplinary optimization,2004,27(5):371-383.
• [16] GINSBOURGER D,RICHE R L,CARRARO L.Kriging is well-suited to parallelize optimization[C]//Computational intelligence in expensive optimization problems,Springer,Berlin,Heidelberg,2010:131-162.
• [17] GARCíA-GARCíA J C,GARCíA-RóDENAS R,CODINA E.A surrogate-based cooperative optimization framework for computationally expensive black-box problems[J].Optimization and Engineering,2020,21(3):1053-1093.
• [18] SUN G,TIAN Y,WANG R,et al.Parallelized multiobjective efficient global optimization algorithm and its applications[J].Structural and Multidisciplinary Optimization,2020,61(2):763-786.
• [19] 王红涛，竺晓程，杜朝辉.基于 Kriging 代理模型的改进 EGO 算法研究[J].工程设计学报，2009,16(4):266-270.
• [20] ZHAN D,QIAN J,CHENG Y.Pseudo expected improvement criterion for parallel EGO algorithm[J].Journal of Global Optimization,2017,68(3):641-662.