EVOLUTIONARY MACHINE LEARNING DRIVEN BIG DATA ANALYSIS AND PROCESSING FOR INDUSTRIAL INTERNET

Wei Chen, Wei Meng and Lingling Zhang
Additional contact information
Wei Chen: School of Computer Science and Technology, Beihua University, Jilin 132013, P. R. China
Wei Meng: School of Computer Science and Technology, Beihua University, Jilin 132013, P. R. China
Lingling Zhang: School of Computer Science and Technology, Beihua University, Jilin 132013, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-14

Abstract: The Industrial Internet is based on the network, the platform is the core, and the security is the guarantee. The Industrial Internet connects all industry elements and the entire industry chain through the large-scale network infrastructure, collects and analyzes industry data in real time, and forms a new application model for a new generation of information communication. With the rapid development of industrial Internet technology, the scale of industrial Internet data will become larger and larger, and the data dimension will become higher and higher. How to efficiently use cluster analysis for industrial Internet big data mining is an urgent problem that needs to be solved. This paper proposes an improved differential evolution particle swarm algorithm for industrial Internet big data clustering analysis. Differential Evolution (DE) strategy can improve the problem that the particle swarm optimization (PSO) algorithm tends to fall into local optimum in the later stage as the number of iterations increases. Considering the influence of the randomness of the arrangement order of the cluster center vectors among the individuals on the learning and updating among individuals, this paper designs a method of adaptively adjusting the arrangement order of the cluster center vectors to optimize the cluster center vector with maximum similarity among individuals. In order to effectively evaluate our method, both industrial and non-industrial datasets are selected. The experimental results verify the feasibility and effectiveness of the proposed algorithm.

Keywords: Industrial Internet; Industrial Big Data; Clustering; Differential Evolution; Particle Swarm Optimization (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X2340100X
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x2340100x

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X2340100X

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().