任务(项目管理)
进化算法
计算机科学
相似性(几何)
进化计算
点(几何)
算法
价值(数学)
度量(数据仓库)
数学优化
人工智能
数学
机器学习
数据挖掘
经济
图像(数学)
几何学
管理
作者
Shaojin Geng,Wuzhao Li,Jiliang Tu,Dongyang Li,Weian Guo,Lei Wang,Qidi Wu
标识
DOI:10.1109/docs60977.2023.10294887
摘要
The evolutionary multi-task optimization algorithm (EMT) enhances the algorithm's performance through information interaction. However, few studies can be found in the current literature utilizing reference points for knowledge transfer. This paper proposes a reference point-assisted explicit multi-objective evolutionary multi-task algorithm (MOEMT-RPA), in which the reference point method is utilized to measure the similarity of optimal solutions. The similarity of tasks is reflected in the number of repetitions of the reference point associated with optimal individuals in different tasks. Compared with advanced comparison algorithms based on the indicators of IGD value and HV value, the experiment on CEC2017 evolutionary multi-task optimization competition benchmarks verified the performance of MOEMT-RPA.
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