Forthcoming

Cat Swarm Optimization with Lévy Flight for Link Load Balancing in SDN

Authors

  • Kwaku Kwarteng Kwame Nkrumah University of Science and Technology, Kumasi, Ghana https://orcid.org/0009-0009-4416-8335
  • Kwame O. Gyasi Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
  • Justice O. Agyemang Kwame Nkrumah University of Science and Technology, Kumasi, Ghana https://orcid.org/0000-0002-9949-3823
  • Kwame Agyekum Kwame Nkrumah University of Science and Technology, Kumasi, Ghana https://orcid.org/0000-0002-7935-9950
  • Kingsford Kwakye Kwame Nkrumah University of Science and Technology, Kumasi, Ghana https://orcid.org/0009-0006-0900-1685
  • Ellis M. Sani Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
  • Emmanuel A. Ampomah Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
  • Kusi A. Bonsu Sunyani Technical University, Sunyani, Ghana https://orcid.org/0000-0002-5474-5206

DOI:

https://doi.org/10.26636/jtit.2025.1.1773

Keywords:

cat swarm optimization, Lévy fligh, load balancing, software-defined networks

Abstract

Efficient network communications with optimal network path selection play a key role in the modern world. Conventional path selection algorithms often face numerous challenges resulting from their limited scope of application. This research proposes a modified swarm intelligence approach, known as cat swarm optimization (CSO) with Lévy flight that is used for network link load balancing and routing optimization. CSO's quick convergence capabilities are suitable for rapid response applications; however, the approach is prone to getting stuck in local optima. Lévy flight enhances search efficiency, thus aiding in escaping local optima. CSO with Lévy flight (CSO-LF) outperforms original CSO and PSO algorithms in terms of solution quality and robustness across various benchmarks. The proposed method has been evaluated in software defined networks (SDN) with nine benchmark functions assessed. CSO-LF achieved the best scores in both the best and worst positions. When used in SDN for link load balancing, CSO-LF demonstrated lower latency and higher throughput than CSO, and lower latency and higher throughput than PSO in a fat tree topology.

Downloads

Download data is not yet available.

References

[1] Nature-Inspired Algorithms and Applied Optimization, ed. by X.-S. Yang, Springer, Cham, 341 p., 2018.
View in Google Scholar

[2] P. Goransson, C. Black, and T. Culver, Software Defined Networks, A Comprehensive Approach, 2nd ed., Elsevier, Cambridge, 2017 (ISBN: 9780128045794).
View in Google Scholar

[3] H. Qi and K. Li, Software Defined Networking Applications in Distributed Datacenters, Springer, Cham, 76 p., 2016.
View in Google Scholar

[4] J. Kennedy and R.C. Eberhart, "Particle Swarm Optimization", Proc. of the IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
View in Google Scholar

[5] X. Hu and R.C. Eberhart, "Adaptive Particle Swarm Optimization: Detection and Response to Dynamic Systems", Proc. of the 2002 Congress on Evolutionary Computation. CEC'02, pp. 1666-1670, 2002.
View in Google Scholar

[6] M. Kong, P. Tian, and Y. Kao, "A New Ant Colony Optimization Algorithm for the Multidimensional Knapsack Problem", Computers & Operations Research, vol. 35, pp. 2672-2863, 2008.
View in Google Scholar

[7] V. Pureza and P.M. Franca, "Vehicle Routing Problems via Tabu Search Metaheuristic", Centre De Recherche Sur Les Transports Publication, pp. 142-149, 1991.
View in Google Scholar

[8] H. Keller, U. Pferschy, and D. Pisinger, Knapsack Problem, Springer, Berlin, 568 p., 2003.
View in Google Scholar

[9] S.C. Chu, P.W. Tsai, and J.S. Pan, "Cat Swarm Optimization", PRICAI 2006: Trends in Artificial Intelligence, pp. 854-858, 2006.
View in Google Scholar

[10] O. Bozorg-Haddad, Advanced Optimization by Nature-Inspired Algorithms, Springer, Singapore, 174 p., 2018.
View in Google Scholar

[11] I. Boussaid, J. Lepagnot, and P. Siarry, "A Survey on Optimization Metaheuristics", Information Sciences, vol. 237, pp. 82-117, 2013.
View in Google Scholar

[12] M. Andresen et al., "Simulated Annealing and Genetic Algorithms for Minimizing Mean Flow Time in an Open Shop", Mathematical and Computer Modelling, vol. 48, pp. 1279-1293, 2008.
View in Google Scholar

[13] N. Feamster, J. Rexford, and E. Zegura, "The Road to SDN: An Intellectual History of Programmable Networks", ACM SIGCOMM Computer Communication Review, vol. 44, pp. 87-98, 2014.
View in Google Scholar

[14] M. Hamdan et al., "A Comprehensive Survey of Load Balancing Techniques on Software-defined Network", Journal of Network and Computer Applications, vol. 174, 2021.
View in Google Scholar

[15] J. Kolodziejczyk and Y. Tarasenko, "Particle Swarm Optimization and Lévy Flight Integration", Procedia Computer Science, vol. 192, pp. 4658-4671, 2021.
View in Google Scholar

[16] R. Bousmaha, R.M. Hamou, and A. Amine, "Automatic Selection of Hidden Neurons and Weights in Neural Networks for Data Classification Using Hybrid Particle Swarm Optimization, Multi-verse Optimization Based on Lévy Flight", Evolutionary Intelligence, vol. 15, pp. 1695-1714, 2022.
View in Google Scholar

[17] X. Liu, G.-G. Wang, and L. Wang, "LSFQPSO: Quantum Particle Swarm Optimization with Optimal Guided Lévy Flight and Straight Flight for Solving Optimization Problems", Engineering with Computers, vol. 38, pp. 4651-4682, 2022.
View in Google Scholar

[18] Y. Liu and B. Cao, "A Novel Ant Colony Optimization Algorithm with Lévy Flight", IEEE Access, vol. 8, pp. 67205-67213, 2020.
View in Google Scholar

[19] Y. Liu, B. Cao, and H. Li, "Improving Ant Colony Optimization Algorithm with Epsilon Greedy and Lévy Flight", Complex and Intelligent Systems, vol. 7, pp. 1711-1722, 2021.
View in Google Scholar

[20] Z. Zhang, Z. Xu, S. Luan, and X. Li, "A Hybrid Max-min Ant System by Lévy Flight and Opposition-based Learning", International Journal of Pattern Recognition and Artificial Intelligence, vol. 35, 2021.
View in Google Scholar

[21] S. Verma, S.P. Sahu, and T.P. Sahu, "MCSO: Lévy’s Flight Guided Modified Chicken Swarm Optimization", IETE Journal of Research, vol. 70, 2024.
View in Google Scholar

[22] A. Yonar and N.Y. Pehlivan, "Artificial Bee Colony with Lévy Flights for Parameter Estimation of 3-p Weibull Distribution", Iranian Journal of Science and Technology, vol. 44, pp. 851-864, 2020.
View in Google Scholar

[23] Y. Chen, J. Xi, H. Wang, and X. Liu, "Grey Wolf Optimization Algorithm Based on Dynamically Adjusting Inertial Weight and Lévy Flight Strategy", Evolutionary Intelligence, vol. 16, pp. 917-927, 2023.
View in Google Scholar

[24] X.-S. Yang, Nature-Inspired Optimization Algorithms, Elsevier, Waltham, 222 p., 2014.
View in Google Scholar

[25] X.-S. Yang, Nature-inspired Algorithms and Applied Optimization, Springer, Cham, 341 p., 2018.
View in Google Scholar

[26] A.O. Jefia, S.I. Popoola, and A.A. Atayero, "Software-defined Networking: Current Trends, Challenges, and Future Directions", Proc. of the International Conference on Industrial Engineering and Operations Management, pp. 1677-1685, 2018.
View in Google Scholar

[27] A. Chechkin, R. Metzler, J. Klafter, and V.Y. Gonchar, "Introduction to the Theory of Lévy Flights", in: Anomalous Transport: Foundations and Applications, pp. 129-162, 2008.
View in Google Scholar

[28] A.A. Al-Temeemy, J.W. Spencer, and J.F. Ralph, "Lévy Flights for Improved Ladar Scanning", 2010 IEEE International Conference on Imaging Systems and Techniques, Thessaloniki, Greece, 2010.
View in Google Scholar

[29] S. Surjanovic and D. Bingham, "Optimization Test Problems", Virtual Library of Simulation Experiments: Test Functions and Datasets, SFU [Online] (https://www.sfu.ca/ ssurjano/optimization.html).
View in Google Scholar

Downloads

Published

2025-02-07

Issue

Early View

Section

ARTICLES FROM THIS ISSUE

License

Copyright (c) 2025 Kwaku Kwarteng, Kwame O. Gyasi, Justice O. Agyemang, Kwame Agyekum, Kingsford Kwakye, Ellis M. Sani, Emmanuel A. Ampomah, Kusi A. Bonsu

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. . Kwarteng, “Cat Swarm Optimization with Lévy Flight for Link Load Balancing in SDN”, JTIT, vol. 99, no. 1, Feb. 2025, doi: 10.26636/jtit.2025.1.1773.