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 paper proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm by using a more general meta-knowledge transfer (MKT) method to solve MTOPs more efficiently. The meta-knowledge defined in this paper 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 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 multi-source 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 a multiple populations for 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.