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International Journal of Crowd Science 2021, 5(2): 204-215 https://doi.org/10.1108/IJCS-03-2021-0010
Research paper | Open Access | Issue | Published: 03 June 2021

Crowd evolution method based on intelligence level clustering

Show Author's Information Lulu Ge1,  Zheming Yang2,  Wen Ji3( )
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and North China University of Science and Technology, Tangshan Hebei, China
Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Keywords:
Cluster analysis, Crowd science, Crowd intelligence, Crowd evolution, Intelligence measure
Cite this article:
Ge L, Yang Z, Ji W. Crowd evolution method based on intelligence level clustering. International Journal of Crowd Science, 2021, 5(2): 204-215. https://doi.org/10.1108/IJCS-03-2021-0010
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Abstract Full text About this article
Purpose

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

Design/methodology/approach

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Findings

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

Practical implications

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

Originality/value

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.


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Abstract
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About this article

Crowd evolution method based on intelligence level clustering

Show Author's information Lulu Ge1, Zheming Yang2, Wen Ji3( )
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and North China University of Science and Technology, Tangshan Hebei, China
Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Abstract

Purpose

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

Design/methodology/approach

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Findings

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

Practical implications

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

Originality/value

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

Keywords: Cluster analysis, Crowd science, Crowd intelligence, Crowd evolution, Intelligence measure

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Publication history
Copyright
Acknowledgements
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Publication history

Received: 03 March 2021
Revised: 24 March 2021
Accepted: 30 March 2021
Published: 03 June 2021
Issue date: August 2021

Copyright

© The author(s)

Acknowledgements

Acknowledgements

This work is supported by the National Key R&Program of China (2017YFB1400100), and the National Natural Science Foundation of China (62072440), and the Beijing Natural Science Foundation (4202072)

Rights and permissions

Lulu Ge, Zheming Yang 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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