Ensemble of Domain Adaptation-Based Knowledge Transfer for Evolutionary Multitasking
提出一种集成多个领域自适应方法的知识迁移策略，通过层次聚类和考虑方法效能与多样性的选择机制，提升进化多任务优化算法的性能，实验验证优于现有算法。
Recently, a number of domain adaptation (DA) methods have been proposed for knowledge transfer in evolutionary multitasking (EMT). However, the learned mappings in these methods often have unique biases in representing the connection between source and target tasks. Few studies have paid attention to the complementarity of different mappings in knowledge transfer. To fill this research gap, this article proposes an ensemble method to combine multiple DA methods for knowledge transfer in EMT by considering the efficacy and diversity of these methods. First, a hierarchical clustering method is used to divide the population of each task into multiple clusters. Then, when two parental solutions are selected for knowledge transfer across tasks, the solutions within the same cluster are checked. In particular, if none of these solutions has been transferred before, the efficacy of DA methods is considered first by using roulette wheel selection based on the corresponding performance improvements in the evolutionary optimization process. Otherwise, the diversity of DA methods is emphasized by randomly selecting one of the DA methods for knowledge transfer. The effectiveness of our proposed ensemble method is validated by embedding it into existing state-of-the-art EMT algorithms, and the experimental results show that our algorithm outperforms several recently proposed EMT algorithms on most cases of two multitasking benchmark suites and one practical case.