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Research paper | Open Access

An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence

Jianran Liu1, Bing Liang2, Wen Ji3( )
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Abstract

Purpose

Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.

Design/methodology/approach

In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.

Findings

This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.

Practical implications

Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.

Originality/value

In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.

Keywords

Differential evolutionK-meansAnomaly detectionCrowd intelligenceIntelligence evolution

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International Journal of Crowd Science
Volume 5 Issue 2,
August 2021
Pages 129-142
DOI: 10.1108/IJCS-07-2020-0013
Cite this article:
Liu J, Liang B, Ji W. An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence. International Journal of Crowd Science, 2021, 5(2): 129-142. https://doi.org/10.1108/IJCS-07-2020-0013

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Received: 17 July 2020
Revised: 15 September 2020
Accepted: 22 September 2020
Published: 09 July 2021
© The author(s)

Jianran Liu, Bing Liang and Wen Ji. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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