.png){kind=icon}
返回
A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2022.3210783.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Bi-objective
evolutionary computation
evolutionary many-task optimization (EMaTO)
evolutionary multitask optimization (EMTO)
knowledge transfer
期刊
IF:
12
论文数:
1.8K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization基于元知识转移的差分进化多任务优化算法OAAI
OAAI分享收藏Multipopulation Ant Colony System With Knowledge-Based Local Searches for Multiobjective Supply Chain Configuration基于知识局部搜索的多种群蚁群多目标供应链配置系统
PREAI
分享收藏
查看更多内容