Pheromone-Guided Evolutionary Optimization of Hybrid Network Topologies: An Ant Colony Algorithm
Keywords:
Network Connectivity Framework, Ant Colony Optimization (ACO), Performance Optimization, Scalability Enhancement, Network Security, Adaptive Problem-SolvingAbstract
This manuscript introduces an innovative methodology for the development of a tailored network connectivity framework, which effectively attains optimized performance, scalability, security, and targeted problem-solving capabilities. This is accomplished through the seamless integration of the Ant Colony Optimization (ACO) machine learning algorithm. The proposed system capitalizes on the inherent capacity of Ant Colony Optimization (ACO) to emulate the foraging behavior exhibited by ants, thereby engendering a hybrid topological model tailored for wireless networks. Through the utilization of Ant Colony Optimization (ACO) in the network topology design process, the system adeptly formulates optimal and flexible connections among network nodes in a dynamic manner. The fitness function, which is formulated to incorporate crucial performance metrics, scalability objectives, security measures, and customized problem-solving scenarios, serves as a guiding principle for the ACO algorithm in its pursuit of identifying the most optimal network pathways. Through rigorous experimentation and meticulous evaluation, this system unequivocally demonstrates its efficacy in delivering a meticulously crafted network infrastructure that seamlessly harmonizes performance, scalability, security, and adaptive problem-solving capabilities.
Downloads
References
M. Dorigo, M. Birattari and T. Stutzle, "Ant colony optimization," in IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, Nov. 2006, doi: 10.1109/MCI.2006.329691.
Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4), 353–373. https://doi.org/https://doi.org/10.1016/j.plrev.2005.10.001
Ganesh, L. Massoulie and D. Towsley, "The effect of network topology on the spread of epidemics," Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., Miami, FL, USA, 2005, pp. 1455-1466 vol. 2, doi: 10.1109/INFCOM.2005.1498374.
Bashan, A., Bartsch, R., Kantelhardt, J. et al. Network physiology reveals relations between network topology and physiological function. Nat Commun 3, 702 (2012). https://doi.org/10.1038/ncomms1705
Fencl, T., Burget, P., & Bilek, J. (2011). Network topology design. Control Engineering Practice, 19(11), 1287–1296. https://doi.org/https://doi.org/10.1016/j.conengprac.2011.07.001
Wang, X. F. (2002). Complex Networks: Topology, Dynamics and Synchronization. International JournaL of Bifurcation and Chaos, 12(5), 885–916. https://doi.org/10.1142/S0218127402004802
T. Summers, I. Shames, J. Lygeros and F. Dörfler, "Topology design for optimal network coherence," 2015 European Control Conference (ECC), Linz, Austria, 2015, pp. 575-580, doi: 10.1109/ECC.2015.7330605.
M. Rafiee and A. M. Bayen, "Optimal network topology design in multi-agent systems for efficient average consensus," 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 3877-3883, doi: 10.1109/CDC.2010.5717719.
N. Kamiyama and D. Satoh, "Network Topology Design Using Analytic Hierarchy Process," 2008 IEEE International Conference on Communications, Beijing, China, 2008, pp. 2048-2054, doi: 10.1109/ICC.2008.393.
Khan, S.A., Engelbrecht, A.P. A fuzzy particle swarm optimization algorithm for computer communication network topology design. Appl Intell 36, 161–177 (2012). https://doi.org/10.1007/s10489-010-0251-2
D. Groß and O. Stursberg, "Optimized distributed control and network topology design for interconnected systems," 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA, 2011, pp. 8112-8117, doi: 10.1109/CDC.2011.6161301.s
N. F. Maxemchuk, I. Ouveysi and M. Zukerman, "A quantitative measure for telecommunications networks topology design," in IEEE/ACM Transactions on Networking, vol. 13, no. 4, pp. 731-742, Aug. 2005, doi: 10.1109/TNET.2005.852889.
Ruijie Luo, Robin Matzner, Alessandro Ottino, Georgios Zervas, and Polina Bayvel, "Exploring the relationship among traffic, topology, and throughput: towards a traffic-optimal optical network topology design," J. Opt. Commun. Netw. 15, B1-B10 (2023)
Pierre, S. (1993). Application of artificial intelligence techniques to computer network topology design. Engineering Applications of Artificial Intelligence, 6(5), 465–472. https://doi.org/https://doi.org/10.1016/0952-1976(93)90007-K
Kumar, A., Pathak, R. M., Gupta, Y. P., & Parsaei, H. R. (1995). A genetic algorithm for distributed system topology design. Computers & Industrial Engineering, 28(3), 659–670. https://doi.org/https://doi.org/10.1016/0360-8352(94)00218-C
E. Testi, E. Favarelli, L. Pucci and A. Giorgetti, "Machine Learning for Wireless Network Topology Inference," 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, QLD, Australia, 2019, pp. 1-7, doi: 10.1109/ICSPCS47537.2019.9008717.
Liu, L., Jin, Q., Wang, D., Yu, H., Sun, G., & Luo, S. (2020). PSNet: Reconfigurable network topology design for accelerating parameter server architecture based distributed machine learning. Future Generation Computer Systems, 106, 320–332. https://doi.org/https://doi.org/10.1016/j.future.2020.01.004
Gupta, N., Vaisla, K.S. & Kumar, R. Design of a Structured Hypercube Network Chip Topology Model for Energy Efficiency in Wireless Sensor Network Using Machine Learning. SN COMPUT. SCI. 2, 376 (2021). https://doi.org/10.1007/s42979-021-00766-7
Yu, Y., Hur, T., Jung, J. et al. Deep learning for determining a near-optimal topological design without any iteration. Struct Multidisc Optim 59, 787–799 (2019). https://doi.org/10.1007/s00158-018-2101-5
H. I. Fahmy, G. Develekos and C. Douligeris, "Application of neural networks and machine learning in network design," in IEEE Journal on Selected Areas in Communications, vol. 15, no. 2, pp. 226-237, Feb. 1997, doi: 10.1109/49.552072.
Cao, J., Jin, D., Yang, L., & Dang, J. (2018). Incorporating network structure with node contents for community detection on large networks using deep learning. Neurocomputing, 297, 71–81. https://doi.org/https://doi.org/10.1016/j.neucom.2018.01.065
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
