Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
76-79,111

Multi-objective particle swarm optimization algorithm using differential evolution for feature selection

Li Mina,b
Zhang Guohaoa
Chen Ziliangb
Guo Zhiyongb
Hu Xiaominb
a. School of Information Engineering, b. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0448

Abstract

Feature selection technology plays an important role in big data analysis, image processing, bioinformatics and other fields. In practical applications, the objectives of reducing the classification error rate and reducing the number of extracted features for facilitating the use of subsequent data, are often two conflicting goals. The multi-object particle swarm optimization based on crowding, mutation, dominance for feature selection(CMDPSOFS) was a kind of bi-objective optimization algorithm with the minimal number of features and classification error rate in feature-oriented selection applications. The algorithm used three different mutation mechanisms for maintaining swarm diversity and balancing global and local search capabilities. However, the uniform variation increased the randomness of the algorithm, resulting in the generation of worse solutions, which reduced the convergence speed of the algorithm. This paper proposed an improved CMDPSOFS-Ⅱ algorithm to introduce the mutation and selection operations of differential evolution algorithm into the CMDPSOFS algorithm. The experimental results show that the CMDPSOFS-Ⅱ algorithm is superior to the original method in feature selection and better balances global and local search capabilities.

Foundation Support

国家自然科学基金资助项目(61772142,61574049)
广州市珠江科技新星项目(201806010059)
广东省信息物理融合系统重点实验室资助项目(2016B030301008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0448
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Algorithm Research & Explore
Pages: 76-79,111
Serial Number: 1001-3695(2020)01-016-0076-04

Publish History

[2020-01-05] Printed Article

Cite This Article

李敏, 章国豪, 陈梓樑, 等. 基于差分进化的多目标粒子群特征选择算法 [J]. 计算机应用研究, 2020, 37 (1): 76-79,111. (Li Min, Zhang Guohao, Chen Ziliang, et al. Multi-objective particle swarm optimization algorithm using differential evolution for feature selection [J]. Application Research of Computers, 2020, 37 (1): 76-79,111. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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