Multiobjective Multitask Optimization With Multiple Knowledge Types and Transfer Adaptation
提出一个多知识类型与迁移自适应框架，通过控制知识类型和数量来提升多目标多任务优化中的知识迁移效果，在39个基准问题和6组实际应用中优于10种先进算法。
Evolutionary multitasking (EMT) exploits the correlation among different tasks to help handle them through knowledge transfer (KT) techniques in evolutionary algorithms. In this area, multiobjective multitask optimization (MO-MTO) utilizes EMT to solve multiple multiobjective optimization tasks simultaneously. The key to addressing MO-MTO problems (MO-MTOPs) is to transfer appropriate knowledge among optimization tasks to assist the multiobjective evolutionary process. Both the type and the amount of knowledge can significantly affect the KT process. To achieve better KT behavior, we propose a multiple knowledge types and transfer adaptation (MKTA) framework for handling MO-MTOPs. The MKTA framework incorporates multiple types of knowledge in order to obtain comprehensive KT performance. It also provides transfer adaptation strategies to control: i) the type of knowledge and ii) the amount of knowledge for KT via parameter adaptation approaches, thereby mitigating negative KT. Furthermore, we propose an evolution-path-model-based knowledge type and incorporate the existing unified-search-space-based knowledge type to form the knowledge pool for MKTA. Finally, The MKTA framework is coupled with a ranking-based differential evolution operator to constitute the complete algorithm MTDE-MKTA. In the experimental study, MTDE-MKTA outperformed 10 advanced algorithms on 39 benchmark MO-MTOPs and six groups of real-world application problems.