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 language | English |
---|---|
Title of host publication | 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 8 |
ISBN (Electronic) | 9798350308365 |
DOIs | |
Publication status | Published - 2024 |
Event | 13th IEEE Congress on Evolutionary Computation, CEC 2024 - Yokohama, Japan, Yokohama, Japan Duration: 30 Jun 2024 → 5 Jul 2024 |
Publication series
Name | 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings |
---|
Conference
Conference | 13th IEEE Congress on Evolutionary Computation, CEC 2024 |
---|---|
Country/Territory | Japan |
City | Yokohama |
Period | 30/06/24 → 5/07/24 |
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