A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Many-task optimization problem (MaTOP) is a kind of challenging multitask optimization problem with more than three tasks. Two significant issues in solving MaTOPs are measuring intertask similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the intertask similarity because the intertask similarity is a concept with multiple different aspects. To address this limitation, this article proposes a bi-objective knowledge transfer (BoKT) framework, which aims first to accurately measure different types of intertask similarity using two different measurements and second to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure intertask similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of intertask similarity. We compare the BoKT framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world MaTOPs with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.