A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

• Published in: IEEE transactions on evolutionary computation 2022-08, Vol.26 (4), p.719-734
• Main Authors: Li, Jian-Yu, Zhan, Zhi-Hui, Tan, Kay Chen, Zhang, Jun
• Format: Article
• Language: English
• Subjects: Evolutionary algorithms; Evolutionary computation; Evolutionary computation (EC); Knowledge; Knowledge management; Knowledge transfer; meta-knowledge transfer (MKT); multitask optimization problem (MTOP); Optimization; Robots; Search problems; Searching; Sociology; Statistics; Task analysis
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2021.3131236

Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality solution from one task to other tasks to enhance the optimization ability, which, however, may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this article proposes a meta-knowledge transfer (MKT)-based differential evolution (MKTDE) algorithm by using a more general MKT method to solve MTOPs more efficiently. The meta-knowledge defined in this article refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of " knowledge of knowledge ," which denotes the knowledge of " how to solve problem via evolution " and " the feature/way/method of evolving high-quality solution ." The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multisource data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is multiple populations for the multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms.