EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 76 (2025) ITM Web Conf., 76 (2025) 05008 References
Open Access
Issue
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
Article Number 05008
Number of page(s) 9
Section Emerging Technologies & Computing
DOI https://doi.org/10.1051/itmconf/20257605008
Published online 25 March 2025
  1. Pham, Q.-V., Nguyen, D. C., Mirjalili, S., Hoang, D. T., Nguyen, D. N., Pathirana, P. N., & Hwang, W.-J. (2020). Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications. arXiv preprint arXiv:2007.15221. [Google Scholar]
  2. Peltier, D. W. III, Kaminer, I., Clark, A., & Orescanin, M. (2024). Swarm Characteristics Classification Using Neural Networks. arXiv preprint arXiv:2403.19572. [Google Scholar]
  3. Shammar, E., Cui, X., & Al-qaness, M. A. A. (2024). Swarm Learning: A Survey of Concepts, Applications, and Trends. arXiv preprint arXiv:2405.00556. [Google Scholar]
  4. Zervoudakis, K., & Tsafarakis, S. (2020). A Mayfly Optimization Algorithm. Computers & Industrial Engineering, 139, 106180. https://doi.org/10.1016/j.cie.2019.106180 [Google Scholar]
  5. Harifi, S., Mohammadzadeh, J., Khalilian, M., & Ebrahimnejad, S. (2019). Emperor Penguins Colony: A New Metaheuristic Algorithm for Optimization. Evolutionary Intelligence, 12(2), 171–185. https://doi.org/10.1007/s12065-019-00235-2 [Google Scholar]
  6. Azizi, M., Aickelin, U., Khorshidi, H. A., & Baghalzadeh Shishehgarkhaneh, M. (2023). Energy Valley Optimizer: A Novel Metaheuristic Algorithm for Global and Engineering Optimization. Scientific Reports, 13(1), 1–19. https://doi.org/10.1038/s41598-023-31456-0 [CrossRef] [PubMed] [Google Scholar]
  7. Castellanos, A., Cruz-Reyes, L., Fernández, E., Rivera, G., Gomez-Santillan, C., & Rangel-Valdez, N. (2022). Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation. Mathematics, 10(3), 322. https://doi.org/10.3390/math10030322 [Google Scholar]
  8. Skorupová, N., Raunigr, P., & Bujok, P. (2022). Usage of Selected Swarm Intelligence Algorithms for Piecewise Linearization. Mathematics, 10(5), 808. https://doi.org/10.3390/math10050808 [Google Scholar]
  9. Dong, J. (2023). Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”. Mathematics, 11(12), 2624. https://doi.org/10.3390/math11122624 [Google Scholar]
  10. Mugwagwa, A., Chibaya, C., & Bhero, E. (2023). A Survey of Inspiring Swarm Intelligence Models for the Design of a Swarm-Based Ontology for Addressing the Cyber Security Problem. International Journal of Research in Business and Social Science, 12(4), 483–494. https://doi.org/10.20525/ijrbs.v12i4.2473 [Google Scholar]
  11. Ghosh, A., & Das, S. (2021). Swarm Intelligence Algorithms and Applications: A Comprehensive Survey. International Journal of Computer Applications, 174(30), 1–15. https://doi.org/10.5120/ijca2021921825 [Google Scholar]
  12. Mirjalili, S., & Lewis, A. (2019). Advances in Swarm Intelligence: From Principles to Practice. Springer. https://doi.org/10.1007/978-3-030-21922-5 [Google Scholar]
  13. Yang, X.-S., & He, X. (2019). Swarm Intelligence and Evolutionary Computation: Algorithms, Analyses, and Applications. Springer. https://doi.org/10.1007/978-3-319-93025-1 [Google Scholar]
  14. Beni, G., & Wang, J. (2019). Swarm Intelligence in Cellular Robotic Systems. In Proceedings of the NATO Advanced Workshop on Robots and Biological Systems (pp. 703–712). Springer. https://doi.org/10.1007/978-3-642-76153-9_38 [Google Scholar]
  15. Dorigo, M., & Stützle, T. (2019). Ant Colony Optimization: Overview and Recent Advances. In Handbook of Metaheuristics (pp. 311–351). Springer. https://doi.org/10.1007/978-1-4419-1665-5_10 [CrossRef] [Google Scholar]
  16. Kennedy, J., & Eberhart, R. (2019). Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks (pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968 [Google Scholar]
  17. Karaboga, D., & Akay, B. (2019). A Survey: Algorithms Simulating Bee Swarm Intelligence. Artificial Intelligence Review, 31(1-4), 61–85. https://doi.org/10.1007/s10462-009-9127-4 [Google Scholar]
  18. Yang, X.-S. (2020). Nature-Inspired Optimization Algorithms. Elsevier. https://doi.org/10.1016/C2018-0-00164-5 [Google Scholar]
  19. Blum, C., & Li, X. (2020). Swarm Intelligence in Optimization. In Springer Handbook of Computational Intelligence (pp. 43–85). Springer. https://doi.org/10.1007/978-3-662-43505-2_3 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.