一种基于混合差分进化和K-means聚类的人群智能异常检测方法

Q2 Decision Sciences
Jianran Liu;Bing Liang;Wen Ji
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引用次数: 2

摘要

目的人工智能正在逐渐渗透到人类社会。在网络时代,人类与人工智能,甚至人工智能之间的互动变得越来越复杂。因此,有必要对人群智能网络的演化进行动态描述和干预。本文旨在检测智能进化早期的异常主体。设计/方法论/方法本文采用差分进化(DE)和K-means聚类方法对进化趋势异常的人群智能进行检测。发现本研究将群体智能的进化过程抽象为DE的求解过程,并使用K-means聚类来识别智能进化早期不利于进化的个体。实验表明,即使在实际应用的复杂人群智能交互环境中,我们提出的方法也能够尽早发现没有进化趋势的个体智能。因此,它可以避免时间和计算资源的浪费。独创性/价值本文将DE和K-means聚类相结合,分析了人群智能交互的演化过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence
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.
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来源期刊
International Journal of Crowd Science
International Journal of Crowd Science Decision Sciences-Decision Sciences (miscellaneous)
CiteScore
2.70
自引率
0.00%
发文量
20
审稿时长
24 weeks
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