Bilevel single-objective optimization algorithm based on transfer learning

被引：0
|
作者：
Yang N. [1 ]
Liu H. [2 ]
机构：
[1] School of Automation, Guangdong University of Technology, Guangzhou
[2] School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2022年 / 50卷 / 05期
关键词：
Bilevel optimization; Cluster; Constrained optimization; Evolutionary algorithm; Transfer learning;
D O I：
10.13245/j.hust.220524
中图分类号：
学科分类号：
摘要：
A bilevel optimization algorithm based on transfer learning (BLOA-TF) was proposed to solve bilevel single-objective optimization problems (BLSOPs), which integrated the idea of transfer learning in the field of machine learning. First, the representative individuals were selected by a clustering algorithm to perform the lower-level optimization, and the obtained lower-level optimization information was recorded by an archive set. Then, the recorded lower-level optimization information was transferred to other individuals without performing the lower-level optimization so that the whole optimization process was accelerated with the computing cost effectively reduced. Finally, the proposed BLOA-TF was compared with a traditional nested bilevel optimization algorithm on 12 standard test problems, and experimental results validated the effectiveness of the proposed BLOA-TF for dealing with BLSOPs. © 2022, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：143 / 148
页数：5
相关论文
共 20 条
  • [1] 1, (2022)
  • [2] SHI C, JIE L, ZHANG G., An extended Kuhn-Tucker approach for linear bilevel programming, Applied Mathematics & Computation, 162, 1, pp. 51-63, (2005)
  • [3] ONAL H., A modified simplex approach for solving bilevel linear programming problems, European Journal of Operational Research, 67, 1, pp. 126-135, (1993)
  • [4] VICENTE L, SAVARD G, JUDICE J., Descent approaches for quadratic bilevel programming, Journal of Optimization Theory and Applications, 81, 2, pp. 379-399, (1994)
  • [5] MATHIEU R, PITTARD L, ANANDALINGAM G., Genetic algorithm based approach to bi-level linear programming, RAIRO-Operations Research, 28, 1, pp. 1-21, (1994)
  • [6] LI X Y, TIAN P, MIN X P., A hierarchical particle swarm optimization for solving bilevel programming problems, Proc of International Conference on Artificial Intelligence and Soft Computing, pp. 1169-1178, (2006)
  • [7] ANGELO J S, KREMPSER E, BARBOSA H J C., Differential evolution for bilevel programming, Proc of 2013 IEEE Congress on Evolutionary Computation, pp. 470-477, (2013)
  • [8] ANGELO J S, BARBOSA H., A study on the use of heuristics to solve a bilevel programming problem, International Transactions in Operational Research, 22, 5, pp. 861-882, (2015)
  • [9] HE X, ZHOU Y, CHEN Z., Evolutionary bilevel optimization based on covariance matrix adaptation, IEEE Transactions on Evolutionary Computation, 23, 2, pp. 258-272, (2019)
  • [10] HUANG P Q, WANG Y., A framework for scalable bilevel optimization: identifying and utilizing the interactions between upper-level and lower-level variables, IEEE Transactions on Evolutionary Computation, 24, 6, pp. 1150-1163, (2020)
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