计算机科学
任务(项目管理)
模式(遗传算法)
进化算法
渡线
人工智能
相似性(几何)
最优化问题
机器学习
进化计算
算法
图像(数学)
经济
管理
作者
Yongliang Chen,Jinghui Zhong,Liang Feng,Mengjie Zhang
出处
期刊:IEEE transactions on emerging topics in computational intelligence
[Institute of Electrical and Electronics Engineers]
日期:2020-06-01
卷期号:4 (3): 369-384
被引量:75
标识
DOI:10.1109/tetci.2019.2916051
摘要
Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, an adaptive selection mechanism is proposed to select suitable “assisted” task for a given task by considering the similarity between tasks and the accumulated rewards of knowledge transfer during the evolution. Besides, a knowledge transfer schema via crossover is adopted to exchange information among tasks to improve the search efficiency. In addition, to facilitate measuring similarity between tasks and transferring knowledge among tasks that arrive at different time instances, multiple archives are integrated with the proposed MaTEA. Experiments on both single-objective and multi-objective optimization problems have demonstrated that the proposed MaTEA can outperform the state-of-the-art multi-task evolutionary algorithms, in terms of search efficiency and solution accuracy. Besides, the proposed MaTEA is also capable of solving dynamic many-task optimization where tasks arrive at different time instances.
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