文献类型：期刊文献

英文题名：An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimization

作者：Zhang, Xi[1];Yu, Guo[1,2];Jin, Yaochu[3,4];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Nanjing Tech Univ, Inst Intelligent Mfg, Nanjing 211816, Peoples R China;[3]Bielefeld Univ, Fac Technol, Chair Nat Inspired Comp & Engn, D-33619 Bielefeld, Germany;[4]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, England

年份：2023

卷号：538

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20231613902460);WOS:【SCI-EXPANDED(收录号:WOS:000981326400001)】；

基金：This work was supported by National Natural Science Founda- tion of China (Key Program: 62136003) , National Natural Science Foundation of China (62173144, 62103150) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and China Postdoctoral Science Foundation (2021M691012) .

语种：英文

外文关键词：Dynamic multi-objective optimization; Transfer learning; Gaussian process; Surrogate assisted evolutionary algorithm

摘要：Expensive dynamic multi-objective optimization problems (EDMOPs) is one kind of DMOPs where the objectives change over time and the function evaluations commonly involve computationally intensive simulations or costly physical experiments. Hence, the key to solve EDMOPs is to quickly and accurately track the time-varying Pareto optimal fronts under the limit of small number of function evaluations, in which how to augment enough training data to build informative surrogate models and manage the mod-els during the search process. To overcome the issue, we propose a transfer learning based surrogate assisted evolutionary algorithm (TrSA-DMOEA) to efficiently solve EDMOPs. Specifically, when a change occurs, we propose a knee point-based manifold transfer learning method based on geodesic flow kernel, which exploits the knowledge from previous high-quality knee solutions to augment the training data for building Gaussian process models, thereby improving the computational complexity and the quality of solutions. Moreover, to efficiently find the optima with limited budget of function evaluations, a novel surrogate-assisted mechanism based on an adaptive acquisition function is introduced, which achieves a balance between convergence and diversity by adaptively adjusting the weights of the angle -penalized distance and average uncertainty at different search stages. By comparing with state-of-the-art algorithms on widely used test problems, the experimental results demonstrate that the proposed method outperforms others and is able to efficiently solve EDMOPs.(c) 2023 Elsevier B.V. All rights reserved.