A Review on Evolutionary Multiform Transfer Optimization

Yinglan FENG, Liang FENG, Xiaoming XUE, Sam KWONG, Kay Chen TAN

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

Abstract

Evolutionary transfer optimization (ETO), which combines evolutionary algorithms with knowledge transfer across related tasks to enhance search performance, has gained widespread attention from researchers in recent years. Multiform transfer optimization (MFTO) stands out as a representative transfer paradigm of ETO, aiming to exploit alternative formulations of the target task of interest. By leveraging useful knowledge acquired from alternative formulations to assist in solving the target task, MFTO has proven effective in tackling complex optimization problems, contributing to the growth of MFTO research. This paper provides a review of existing research progress in MFTO. Firstly, we introduce the fundamental aspects of MFTO, including the general framework and core components. Subsequently, we summarize the advances in MFTO from the perspectives of problems to be solved and the way of constructing alternative formulations. Lastly, we discuss promising future research directions. It is hoped that this survey can provide a thorough understanding of the MFTO framework and facilitate the development of more advanced MFTO algorithms and applications.
Original languageEnglish
Title of host publication2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9798350308365
DOIs
Publication statusPublished - 2024
Event13th IEEE Congress on Evolutionary Computation, CEC 2024 - Yokohama, Japan, Yokohama, Japan
Duration: 30 Jun 20245 Jul 2024

Publication series

Name2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings

Conference

Conference13th IEEE Congress on Evolutionary Computation, CEC 2024
Country/TerritoryJapan
CityYokohama
Period30/06/245/07/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This work is partially supported by the National Key R&D Program of China (2022YFC3801700), the Research Grants Council of the Hong Kong SAR (Grant No. PolyU11211521, PolyU15218622, PolyU15215623, PolyU25216423, and C5052-23G), The Hong Kong Polytechnic University (Project IDs: P0039734, P0035379, P0043563, and P0046094), and the National Natural Science Foundation of China (Grant No. U21A20512, and 62306259).

Keywords

  • Evolutionary Transfer Optimization
  • Knowledge Transfer
  • Multiform Optimization
  • Problem Formulation

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