A Review on Evolutionary Multiform Transfer Optimization

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.