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

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.

Automated summary

Knowledge transfer plays a vital role in solving multitask optimization problems. This article proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm, which efficiently solves MTOPs using a more general approach. By transferring meta-knowledge, the MKTDE algorithm effectively associates different tasks' heterogeneous multisource data to solve MTOPs more efficiently. Two novel methods, multiple populations for the multiple tasks framework and elite solution transfer, further enhance the MKTDE algorithm. Extensive experiments validate the superior performance of the proposed algorithm compared to state-of-the-art approaches.