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  A Novel Intelligent Ant Colony System Based on Blockchain

Wu, W., Peng, H., Li, L., Stanley, H. E., Wang, L., Kurths, J. (2022): A Novel Intelligent Ant Colony System Based on Blockchain. - In: Tan, Y., Shi, Y., Niu, B. (Eds.), Advances in Swarm Intelligence, (Lecture Notes in Computer Science ; 13344), Cham : Springer, 230-246.
https://doi.org/10.1007/978-3-031-09677-8_20

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 Creators:
Wu, Wei1, Author
Peng, Haipeng1, Author
Li, Lixiang1, Author
Stanley, H. Eugene1, Author
Wang, Licheng1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Swarm intelligence occurs when the collective behavior of low-level individuals and their local interactions form an overall pattern of uniform function. Incorporating swarm intelligence allows us to disregard global models when we explore collective cooperation systems that lack any central control. Blockchain is a key technology in the functioning of Bitcoin and combines network and cryptographic algorithms. A group of agents agrees on a particular status and records the protocol without controlling it. Blockchain and other distributed systems, such as ant colony systems, allow the building of “ants” that are more secure, flexible, and successful. We use the principle of blockchain technology and carry out ant colony research to solve three urgent problems. We use new security protocols, system implementations, and business models to generate ant swarm system scenarios. Finally we combine these two technologies to solve the problems of limitation and reduced future potential. Our work opens the door to new business models and approaches that allow ant colony technologies to be applied to a wide range of market applications.

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Language(s): eng - English
 Dates: 2022-06-262022
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/978-3-031-09677-8_20
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Advances in Swarm Intelligence
Source Genre: Book
 Creator(s):
Tan, Ying1, Editor
Shi, Yuhui1, Editor
Niu, Ben1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Cham : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 230 - 246 Identifier: ISBN: 978-3-031-09676-1
ISBN: 978-3-031-09677-8
DOI: 10.1007/978-3-031-09677-8

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 13344 Sequence Number: - Start / End Page: - Identifier: -