Directional combination learning for multitask optimization

Knowledge transfer (KT) plays an essential role in evolutionary multitask optimization (EMTO), whereas ”what to transfer” and ”how to transfer” are the key focuses that influence the KT performance. For the issue ”what to transfer”, many EMTO algorithms often transfer the top individuals directly during the KT process. However, the transferred individual information is not always effective, especially when the tasks own the low similarity. For the issue ”how to transfer”, many KT methods mainly focus on learning from all dimensions in a single individual, causing the lack of learning diversity. Therefore, in this paper, we propose a novel directional combination learning method for multitask optimization, termed as DCLMTO, tries to address these two issues effectively. Specifically, for the issue ”what to transfer”, a directional learning (DL) strategy is developed to deeply extract the useful directional knowledge, which is behind the individual information and shows the trend of moving toward better position, for KT preparation. For the issue ”how to transfer”, a combination learning (CL) strategy is designed to fully combine the superiorities of different individuals for enriching the learning diversity. Moreover, DCLMTO can be easily extended to many-task optimization problems (MaTOPs) and can directly avoid the difficulty of task selection, showing its convenience and scalability. Experimental results demonstrate that DCLMTO significantly outperforms other state-of-the-art EMTO and EMaTO algorithms on the multitask benchmarks CEC17 and CEC22, and the many-task benchmarks CEC19 and WCCI20. Finally, DCLMTO is applied to a real-world multitask planar kinematic arm control application, further demonstrating its applicability.