Open Access
Issue
Published:
23 July 2020
A new simulation framework for crowd collaborations
)
Download citation
642
Views
18
Downloads
4
Crossref
N/A
WoS
4
Scopus
N/A
CSCD
Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed.
In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them.
To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation.
Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.
menu
A new simulation framework for crowd collaborations
)
Abstract
Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed.
In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them.
To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation.
Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.
References(16)
Chai, Y., Miao, C., Sun, B., Zheng, Y. and Li, Q. (2017), “Crowd science and engineering: concept and research framework”, International Journal of Crowd Science, Vol. 1 No. 1, pp. 2-8.
Chu, S.-C., Roddick, J.F. and Su, C.-J. (2004), “Constrained ant colony optimization for data clustering”, J. Artif.Intell, pp. 534-554.
Feng, J., Li, G. and Feng, J. (2015), “Review of crowdsourcing technology research”, Journal of Computer Science, Vol. 38 No. 9, pp. 1713-1726.
Ikediego, H.O., İlkan, M., Abubakar, A.M. and Bekun, F.V. (2018), “Crowd-sourcing (who, why and what)”, International Journal of Crowd Science, Vol. 2 No. 1.
Tan, T., Cai, S. and Hu, M. (2011), “Research status of crowdsourcing abroad”, Journal of Wuhan University of Technology (Information and Management Engineering Edition), Vol. 2, pp. 97-100.
Wang, M., Zhu, Y. and He, X. (2005), “Review of group intelligence research”, Computer Engineering, Vol. 22, pp. 204-206.
Xia, E., Zhao, X. and Li, S. (2015), “Current situation and trend of overseas crowdsourcing research”, Technology and Economy, Vol. 34 No. 1, pp. 28-36.
Xie, H. and Zhang, Z. (2014), “Review of supply chain collaboration research and theoretical model construction”, Journal of Shandong University of Business and Industry, Vol. 28 No. 2, pp. 71-77.
Yu, S., Li, J. and Lv, X. (2009), “Collaborative optimization of supply chain based on simulation”, Microcomputer Application, Vol. 10, pp. 76-82.
Zhang, H. (2015), “Analysis of supply chain cooperation research”, Logistics Technology, Vol. 38 No. 3, pp. 48-49.
Zhao, J., Zhang, X., Li, J., et al. (2019), “Group intelligence two research review”, Computer Engineering, Vol. 12.
Publication history
Copyright
Acknowledgements
This work is supported by the National Key R&D Program of China (Grant No. 2017YFB1400105).
Rights and permissions
Rui Yang and Hongbo Sun. 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
FEEDBACK 
TOP