|Table of Contents|

Surrogate-Assisted Evolution Algorithm Based on Label Propagation Guidance and Region Adaptive Integration(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年04期
Page:
1-16
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Surrogate-Assisted Evolution Algorithm Based on Label Propagation Guidance and Region Adaptive Integration
Author(s):
Li Erchao; Cui Tianchao
(College of Electrical Engineering and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China)
Keywords:
data-driven; surrogate model; label propagation; adaptive integration; filling criterion
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2024.04.001
Abstract:
Agent-assisted evolutionary algorithms have been widely used to solve computationally expensive optimization problems. In order to improve the ability and efficiency of the pre-screening solution of the agent model under the condition of limited computing resources,the paper proposes an agent-assisted evolution algorithm based on label propagation guidance and regional adaptive integration,which is divided into two stages:global search and local search. In the global search stage,the multiple screening criteria(MSC)is proposed. Two proxy models are used to predict the fitness values of the classified populations,and the populations are screened again according to the best fitness of the parents and offspring. The local search phase uses the SMOTE(synthetic minority over-sampling technique)method to generate dynamic local populations and test samples,select individuals for evaluation based on adaptive integration of the performance of the two proxy models in the most promising areas. Finally,the proposed method is compared with other advanced agent assisted evolution algorithms in 8 test problems and airfoil design optimization problems,showing that the proposed method has better convergence.

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.

Memo

Memo:
-
Last Update: 2024-12-15