Guo-Yin Wang, Dong-Dong Cheng, De-You Xia, Hai-Huan Jiang. Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence. Machine Intelligence Research, vol. 20, no. 1, pp.121-144, 2023. https://doi.org/10.1007/s11633-022-1367-7
Citation: Guo-Yin Wang, Dong-Dong Cheng, De-You Xia, Hai-Huan Jiang. Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence. Machine Intelligence Research, vol. 20, no. 1, pp.121-144, 2023. https://doi.org/10.1007/s11633-022-1367-7

Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

doi: 10.1007/s11633-022-1367-7
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  • Author Bio:

    Guo-Yin Wang received the B. Sc. and M. Sc. degrees in computer software from Xi′an Jiaotong University, China in 1992 and 1994, respectively, and the Ph. D. degree in computer organization and system structure from Xi′an Jiaotong University, China in 1996. He was at University of North Texas, USA, and University of Regina, Canada, as a visiting scholar during 1998–1999. Since 1996, he has been at the Chongqing University of Posts and Telecommunications, China, where he is currently a professor, the vice president of the university, and the director of the Chongqing Key Laboratory of Computational Intelligence. He was the director of the Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), China, during 2011–2017. He is the author of 13 books and the editor of dozens of proceedings of national and international conferences and has more than 300 reviewed research publications. He was the president of the International Rough Set Society (IRSS) from 2014 to 2017, and is currently a vice president of the Chinese Association for Artificial Intelligence (CAAI) and a council member of the China Computer Federation (CCF). He is a fellow of IRSS, CAAI and CCF. His research interests include rough sets, granular computing, knowledge technology, data mining, neural networks, and cognitive computing. E-mail: wanggy@cqupt.edu.cn (Corresponding author) ORCID iD: 0000-0002-8521-5232

    Dong-Dong Cheng received the B. Sc. degree in computer science from Chongqing Normal University, China in 2013, and the Ph. D. degree in software engineering from Chongqing University, China in 2018. She is currently an associate professor of College of Big Data and Intelligent Engineering at Yangtze Normal University, and she is also a postdoctoral fellow at the Chongqing University of Posts and Telecommunications, China. She has published more than 10 academic papers in top international journals, such as IEEE TKDE, IEEE TNNLS, and IEEE TSMC-S. She was selected into the Chongqing Postdoctoral Innovative Talent Support Program in 2021. Her research interests include clustering analysis, data mining and swarm intelligence. E-mail: cdd@yznu.edu.cn

    De-You Xia received the M. Sc. degree in systems science from the Chongqing University of Posts and Telecommunications, China in 2018. He is currently a Ph. D. degree candidate in computer science and technology at Chongqing University of Posts and Telecommunications, China. His research interests include machine learning, granular computing, rough sets and swarm intelligence.E-mail: 1025968052@qq.com

    Hai-Huan Jiang received the M. Sc. degree in systems science from Chongqing University of Posts and Telecommunications, China in 2020. She is currently a Ph.D. degree candidate in computer science and technology at Chongqing University of Posts and Telecommunications, China. Her research interests include granular computing, rough sets and swarm intelligence.E-mail: haihuan_jiang@163.com

  • Received Date: 2022-05-25
  • Accepted Date: 2022-08-08
  • Swarm intelligence has become a hot research field of artificial intelligence. Considering the importance of swarm intelligence for the future development of artificial intelligence, we discuss and analyze swarm intelligence from a broader and deeper perspective. In a broader sense, we are talking about not only bio-inspired swarm intelligence, but also human-machine hybrid swarm intelligence. In a deeper sense, we discuss the research using a three-layer hierarchy: in the first layer, we divide the research of swarm intelligence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence; in the second layer, the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence; and in the third layer, we review single-population, multi-population and human-machine hybrid models from different perspectives. Single-population swarm intelligence is inspired by biological intelligence. To further solve complex optimization problems, researchers have made preliminary explorations in multi-population swarm intelligence. However, it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive intelligent behavior that meets the needs of human cognition. Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence. In addition to single-population swarm intelligence, we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper. We also discuss the applications of swarm intelligence in optimization, big data analysis, unmanned systems and other fields. Finally, we discuss future research directions and key issues to be studied in swarm intelligence.

     

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