[1]包建阳,吕秋月,孙越泓.基于两点模型选择的离线数据驱动进化优化算法[J].南京师大学报(自然科学版),2022,45(03):1-8.[doi:10.3969/j.issn.1001-4616.2022.03.001]
　Bao Jianyang,Lv Qiuyue,Sun Yuehong.Offline Data-Driven Evolutionary Algorithm Using Best Point and Uncertainty Point to Guide Model Selection[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(03):1-8.[doi:10.3969/j.issn.1001-4616.2022.03.001]
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基于两点模型选择的离线数据驱动进化优化算法()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第45卷
期数:
2022年03期
页码:
1-8
栏目:
数学
出版日期:
2022-09-15

文章信息/Info

Title:
Offline Data-Driven Evolutionary Algorithm Using Best Point and Uncertainty Point to Guide Model Selection
文章编号:
1001-4616(2022)03-0001-08
作者:
包建阳1; 吕秋月1; 孙越泓1; 2
(1.南京师范大学数学科学学院,江苏 南京 210023)(2.江苏省大规模复杂系统数值模拟重点实验室,江苏 南京 210023)
Author(s):
Bao Jianyang1; Lv Qiuyue1; Sun Yuehong1; 2
(1.School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems,Nanjing 210023,China)
关键词:
集成学习; 进化算法; 离线数据驱动优化; 代理模型
Keywords:
ensemble learning; evolutionary algorithm; offline data-driven optimization; surrogate model
分类号:
TP18
DOI:
10.3969/j.issn.1001-4616.2022.03.001
文献标志码:
A
摘要:
基于两点模型选择的离线数据驱动进化优化算法主要用于解决目标计算复杂度高的离线优化问题. 在模型建立过程中,建立多个代理模型,而后运用模型选择策略,从中选择部分代理模型,组成集成模型. 同时,模型选择策略概率被采用,用来提高算法通用性和减少时间复杂度. 该算法在常见的基准测试函数上进行了数值实验,与其他先进的算法进行了比较,实验结果表明,新算法更具有优势.
Abstract:
The off-line data-driven evolutionary optimization algorithm based on two-point model selection is mainly used to solve off-line optimization problems with high computational complexity. In the process of model establishment,several agent models are established,and then some agent models are selected by model selection strategy to form an integrated model. At the same time,the probability of model selection strategy is adopted to improve the generality of the algorithm and reduce the time complexity. Numerical experiments are carried out on common benchmark test functions,and the experimental results show that the new algorithm has more advantages than other advanced algorithms.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-10-27.
基金项目:国家自然科学基金项目(11871279).
通讯作者:孙越泓,博士,副教授,研究方向:智能优化及图像处理. E-mail:05234@njnu.edu.cn
更新日期/Last Update: 2022-09-15