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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Prybila, C., Schulte, S., Hochreiner, C., Weber, I.: Runtime verification for business processes utilizing the Bitcoin blockchain. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2017.08.024
Andoni, M., Robu, V., Flynn, D.: Blockchains: crypto-control your own energy supply. Nature 548, 158–158 (2017)
Bag, S., Ruj, S., Sakurai, K.: Bitcoin block withholding attack: analysis and mitigation. IEEE Trans. Inf. Forensics Secur. 12, 1967–1978 (2017)
Qu, Y., Pokhrel, S.R., Garg, S., Gao, L., Xiang, Y.: A blockchained federated learning framework for cognitive computing in industry 4.0 networks. IEEE Trans. Ind. Inform. 17(4), 2964–2973 (2021)
Vangala, A., Sutrala, A.K., Das, A.K., Jo, M.: Smart contract-based blockchain-envisioned authentication scheme for smart farming. IEEE Internet Things J. 8(13), 10792–10806 (2021)
Li, Y., Cao, B., Liang, L., Mao, D., Zhang, L.: Block access control in wireless blockchain network: design, modeling and analysis. IEEE Trans. Veh. Technol. 70(9), 9258–9272 (2021)
Vranken, H.: Sustainability of bitcoin and blockchains. Curr. Opin. Environ. Sustain. 28, 1–9 (2017)
Kennedy, J.: Review of Engelbrecht’s fundamentals of computational swarm intelligence. Genet. Program Evolvable Mach. 8, 107–109 (2007)
Carabaza, S.P., Besada-Portas, E., Lopez-Orozco, J.A., de la Cruz, J.M.: Ant colony optimization for multi-UAV minimum time search in uncertain domains. Appl. Soft Comput. (2017). https://doi.org/10.1016/j.asoc.2017.09.009
Zhang, W., Gong, X., Han, G., Zhao, Y.: An improved ant colony algorithm for path planning in one scenic area with many spots. IEEE Access 5, 13260–13269 (2017)
Boubertakh, H.: Knowledge-based ant colony optimization method to design fuzzy proportional integral derivative controllers. J. Comput. Syst. Sci. Int. 56(4), 681–700 (2017). https://doi.org/10.1134/S1064230717040050
Korb, O.: Efficient ant colony optimization algorithms for structure- and ligand-based drug design. Chem. Cent. J. 3, O10 (2009)
Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)
Zhou, J., et al.: A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security. Appl. Math. Model. 45, 684–704 (2017)
Aste, T., Tasca, P., Di Matteo, T.: Blockchain technologies: the foreseeable impact on society and industry. Computer 50, 18–28 (2017)
Li, X., Jiang, P., Chen, T., Luo, X., Wen, Q.: A Survey on the security of blockchain systems. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2017.08.020
Kamali, M.Z.M., Kumaresan, N., Ratnavel, K.: Solving differential equations with ant colony programming. Appl. Math. Model. 3910, 3150–3163 (2015)
Hsin, H.K., Chang, E.J., Lin, C.A., Wu, A.Y.: Ant colony optimization-based fault-aware routing in mesh-based network-on-chip systems. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 33, 1693–1705 (2014)
Gaifang, D., Xueliang, F., Honghui, L., Pengfei, X.: Cooperative ant colony-genetic algorithm based on spark. Comput. Electr. Eng. 60, 66–75 (2017)
Yue, W., Ma, W., Miao, Q., Wang, S.: Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search. Swarm Evol. Comput. (2017). https://doi.org/10.1016/j.swevo.2017.07.004
Arnay, R., Fumero, F., Sigut, J.: Ant colony optimization-based method for optic cup segmentation in retinal images. Appl. Soft Comput. 52, 409–417 (2017)
Naeem, M., Pareek, U., Lee, D.C.: Swarm intelligence for sensor selection problems. IEEE Sens. J. 128, 2577–2585 (2012)
Yang, Z., Sun, J., Zhang, Y., Wang, Y.: Understanding SaaS adoption from the perspective of organizational. Comput. Hum. Behav. 45, 254–264 (2015)
Byk, J., Del-Claro, K.: Ant-plant interaction in the Neotropical savanna. Popul. Ecol. 53, 327–332 (2011)
Sharif, S., Watson, P., Taheri, J., Nepal, S., Zomaya, A.Y.: Privacy-aware scheduling SaaS in high performance computing environments. IEEE Trans. Parallel Distrib. Syst. 28, 1176–1188 (2017)
Shi, N.: A new proof-of-work mechanism for bitcoin. Financ. Innov. 2(1), 1–8 (2016). https://doi.org/10.1186/s40854-016-0045-6
Jangra, R., Kait, R.: Analysis and comparison among ant system; ant colony system and max-min ant system with different parameters setting. In: 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–4 (2017)
Samanta, C.K., Padhy, S.K., Panigrahi, S.P., Panigrahi, B.K.: Hybrid swarm intelligence methods for energy management in hybrid electric vehicles. IET Electr. Syst. Transp. 3, 22–29 (2013)
Filho, J.C.M., de Souza, R.N., Abrao, T.: Ant colony input parameters optimization for multiuser detection in DS/CDMA systems. IEEE Latin Am. Trans. 12, 1355–1364 (2014)
Acknowledgments
This work is supported by the National Key R&D Program of China (Grant No. 2020YFB1805403), the National Natural Science Foundation of China (Grant No. 62032002), the Natural Science Foundation of Beijing Municipality (Grant No. M21034) and the 111 Project (Grant No. B21049).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-09677-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09676-1
Online ISBN: 978-3-031-09677-8
eBook Packages: Computer ScienceComputer Science (R0)