[1]金 涛,朱 莉,李 豪,等. 基于t-SNE加权和的高维多目标优化算法[J].湖北工业大学学报,2022,(1):40-45.
　JIN Tao,ZHU Li,LI Hao,et al. High-dimensional Multi-objective Optimization Algorithm Based on t-SNE Weighted Sum[J].,2022,(1):40-45.
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 基于t-SNE加权和的高维多目标优化算法()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2022年第1期
页码:
40-45
栏目:
湖北工业大学学报
出版日期:
2022-02-28

文章信息/Info

Title:
 High-dimensional Multi-objective Optimization Algorithm Based on t-SNE Weighted Sum
文章编号:
1003-4684(2022)01-0040-06
作者:
 金 涛;  朱 莉;  李 豪;  汪小豪;  姜成龙
 湖北工业大学电气与电子工程学院， 湖北 武汉 430068
Author(s):
 JIN Tao;  ZHU Li;  LI Hao;  WANG Xiaohao;  JIANG Chenglong
 School of Electrical and Electronic Engineering, Hubei Univ. of Tech. Wuhan 430068, China
关键词:
 ; 高维多目标优化算法;  冗余目标集;  t-SNESUM-NSGAⅡ;  加权和
Keywords:
 ; High-dimensional multi-objective optimization algorithm;  redundant objective set;  t-SNESUM-NSGAⅡ;  weighted summation
分类号:
TP301.6
文献标志码:
A
摘要:
 传统的基于t-SNE的高维多目标优化算法在简化目标集时，虽然可以大大降低算法计算复杂度，但也可能损失目标集中有意义的部分属性，导致算法准确性降低。为此，对冗余目标和初始化种群双方面进行择优保留，提出了一种基于t-SNE加权和的高维多目标优化算法。利用加权和对t-SNE-NSGAⅡ算法处理的冗余目标集进行拟合，保留了部分种群的目标属性，提高了初始种群的质量，提升了算法的准确性，加快了算法收敛速度。实验表明，在目标超过5个时，基于t-SNE加权和的高维多目标优化算法的准确性和收敛性提升明显。当目标为10个时，空间分布度提升了38.7%。
Abstract:
 When the traditional high-dimensional multi-objective optimization algorithm based on t-distributed stochastic neighbor embedding (t-SNE-NSGAⅡ) simplifies the target set, although it can greatly reduce the computational complexity of the algorithm, it may also lose some meaningful attributes of the target set, resulting in the accuracy of the algorithm decreased. Therefore, a high-dimensional multi-objective optimization algorithm based on t-SNE weighted sum was proposed to preserve redundant target and initial population. The weighted sum is used to fit the redundant target set processed by t-SNE-NSGAⅡalgorithm, and the target attributes of part of the population are retained, the quality of the initial population is improved, the accuracy of the algorithm is improved, and the convergence speed of the algorithm is accelerated. Experimental results show that when the number of targets is more than 5, the accuracy and convergence of the high-dimensional multi-objective optimization algorithm based on t-SNE weighted sum are improved obviously. When the number of targets was 10, the spatial distribution increased by 38.7%.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2021-06-20
［基金项目］ 新能源及电网装备安全监测湖北省工程研究中心开放研究基金（HBSKF202124）
［第一作者］ 金 涛（1994-），男，安徽马鞍山人，湖北工业大学硕士研究生，研究方向为大数据
［通信作者］ 朱 莉（1982-），女，湖北武汉人，湖北工业大学副教授，研究方向为人工智能和大数据
更新日期/Last Update: 2022-02-25