CASIA OpenIR  > 学术期刊  > IEEE/CAA Journal of Automatica Sinica
A Two-layer Encoding Learning Swarm Optimizer based on Frequent Itemsets for Sparse Large-scale Multi-objective Optimization
Sheng Qi; Rui Wang; Tao Zhang; Xu Yang; Ruiqing Sun; Ling Wang
Source PublicationIEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2024
Volume11Issue:6Pages:1342-1357
AbstractTraditional large-scale multi-objective optimization algorithms (LSMOEAs) encounter difficulties when dealing with sparse large-scale multi-objective optimization problems (SLMOPs) where most decision variables are zero. As a result, many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately. Nevertheless, existing optimizers often focus on locating non-zero variable positions to optimize the binary variables Mask. However, approximating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized. In data mining, it is common to mine frequent itemsets appearing together in a dataset to reveal the correlation between data. Inspired by this, we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets (TELSO) to address these SLMOPs. TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence. Experimental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms (SLMOEAs) in terms of performance and convergence speed.
KeywordEvolutionary algorithms learning swarm optimization sparse large-scale optimization sparse large-scale multi-objective problems two-layer encoding
DOI10.1109/JAS.2024.124341
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56453
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
Sheng Qi,Rui Wang,Tao Zhang,et al. A Two-layer Encoding Learning Swarm Optimizer based on Frequent Itemsets for Sparse Large-scale Multi-objective Optimization[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(6):1342-1357.
APA Sheng Qi,Rui Wang,Tao Zhang,Xu Yang,Ruiqing Sun,&Ling Wang.(2024).A Two-layer Encoding Learning Swarm Optimizer based on Frequent Itemsets for Sparse Large-scale Multi-objective Optimization.IEEE/CAA Journal of Automatica Sinica,11(6),1342-1357.
MLA Sheng Qi,et al."A Two-layer Encoding Learning Swarm Optimizer based on Frequent Itemsets for Sparse Large-scale Multi-objective Optimization".IEEE/CAA Journal of Automatica Sinica 11.6(2024):1342-1357.
Files in This Item: Download All
File Name/Size DocType Version Access License
JAS-2023-1304.pdf(3038KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sheng Qi]'s Articles
[Rui Wang]'s Articles
[Tao Zhang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sheng Qi]'s Articles
[Rui Wang]'s Articles
[Tao Zhang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sheng Qi]'s Articles
[Rui Wang]'s Articles
[Tao Zhang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: JAS-2023-1304.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.