When and How to Transfer Knowledge in Dynamic Multi-objective Optimization

Gan RUAN, Leandro L. MINKU, Stefan MENZEL, Bernhard SENDHOFF, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

22 Citations (Scopus)

Abstract

Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems. © 2019 IEEE.
Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2034-2041
Number of pages8
ISBN (Print)9781728124858
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes

Bibliographical note

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 766186. The work was also supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shen-zhen Peacock Plan (Grant No. KQTD2016112514355531) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

Keywords

  • dynamic multi-objective optimization
  • evolutionary algorithms
  • prediction-based method.
  • transfer learning

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