Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning for Dynamic Expensive Multi-Objective Optimisation Problems

Xuezhou Fan, Ke Li, Kay Chen Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Dynamic multi-objective optimisation has attracted increasing attention in the evolutionary multi-objective optimisation community in recent years. Comparing to its static counterpart, which has been studied for more than half a century, the involvement of dynamic and uncertain features, including but not limited to the changing Pareto-optimal set, Pareto-optimal front and problem formulation, pose significant more challenges to evolutionary algorithms. This will become even more complicated when the underlying problem involves computationally expensive objective functions which are not rare in many realworld application scenarios. In this paper, we pave an initial step towards the study of dynamic multi-objective optimisation with computationally expensive objective functions. More specifically, we use a surrogate assisted evolutionary algorithm, MOEA/DEGO in particular, as the baseline in order to carry out evolutionary optimisation with a limited amount of function evaluations. Furthermore, instead of restart the MOEA/D-EGO from scratch after each change, we use transfer learning to map the previously archived training data to the current landscape in order to jump start the surrogate model building process. By doing so, we can expect a better adaptation to the new environment. Proof-of-concept experiments fully demonstrate the effectiveness of our proposed method.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728169293
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings


Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow


  • Dynamic multi-objective optimization
  • evolutionary algorithm
  • Gaussian process
  • surrogate modeling
  • transfer learning

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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