Abstract
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 “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">knowledge of knowledge</i> ,” which denotes the knowledge of “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how to solve problem via evolution</i> ” and “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the feature/way/method of evolving high-quality solution</i> .” 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.
Highlights
As a kind of evolutionary transfer optimization [1][2], evolutionary multitask optimization (EMTO) [3] is a newly emerging paradigm that attempts to solve multitask optimization problem (MTOP) through evolutionary computation (EC) algorithms
This paper proposes a meta-knowledge transfer (MKT) method and applies it to the differential evolution (DE) to put forward an meta-knowledge transfer-based differential evolution (MKTDE) algorithm to solve MTOP more efficiently
The MKTDE developed for solving MTOPs is detailed in this part, which is based on the multiple populations for multiple tasks (MPMT) framework with the MKT and elite solution transfer (EST) methods
Summary
As a kind of evolutionary transfer optimization [1][2], evolutionary multitask optimization (EMTO) [3] is a newly emerging paradigm that attempts to solve multitask optimization problem (MTOP) through evolutionary computation (EC) algorithms. This paper proposes a meta-knowledge transfer (MKT) method and applies it to the DE to put forward an MKTDE algorithm to solve MTOP more efficiently. The MPMT is different from existing multi-population methods in that each population in the MPMT is based on the same unified search space, so that the knowledge transfer method like the proposed MKT can be performed efficiently and flexibly among multiple populations. Summarized in the following: First, this paper provides the mathematical definition of meta-knowledge for evolutionary search and proposes an MKT method to solve MTOPs efficiently by transferring meta-knowledge, which can work well even though different tasks have very different task-specific knowledge.
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