A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem


  • T. Akhtar Department of Computer Science and Information Technology, NED University of Engineering & Technology, Pakistan
  • N. G. Haider Department of Computer Science and Information Technology, NED University of Engineering & Technology, Pakistan
  • S. M. Khan Department of Computer Science and IT, NED University of Engineering and Technology, Pakistan
Volume: 12 | Issue: 4 | Pages: 8777-8784 | August 2022 | https://doi.org/10.48084/etasr.4999


Vast amounts of data are transferred through communication networks resulting in node congestion, which varies according to peak usage times. The Glowworm Swarm Optimization (GSO) algorithm is inspired by the rummaging and courtship behavior of glowworms. The glow intensity of glowworms is a measure of fitness that attracts other glowworms in its neighborhood. This work applies the GSO algorithm to the computer network congestion problem in order to lessen the network burden by shifting loads to the fittest neighborhood nodes, thereby enhancing network performance during peak traffic times, when the response of systems on the network would go down. The proposed solution aims to alleviate the burdened nodes, thereby improving the flow of traffic throughout the network, improving the users’ experience and productivity, and efficiency. In this paper, three swarm algorithms, namely Particle Swarm Optimization (PSO), Cuckoo Search (CK), and GSO have been employed to solve the network load balancing problem. The results produced by GSO show improvement of 71.17%, 74.14%, and 84.15% in networks consisting of 50, 100, and 200 nodes in peak hour load, while PSO shows 13.87%, 11.75%, and 23.72%, and CK 10.61%, 3.19%, and 6%. The results prove the superior performance of GSO.


Network congestion, Load Balancing, GSO, throughput, Swarm Intelligence


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H. Ben Alla, S. Ben Alla, A. Touhafi, and A. Ezzati, "A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment," Cluster Computing, vol. 21, no. 4, pp. 1797–1820, Dec. 2018. DOI: https://doi.org/10.1007/s10586-018-2811-x

M. M. Abed and M. Younis, "Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms," Baghdad Science Journal, vol. 16, pp. 130–139, Mar. 2019. DOI: https://doi.org/10.21123/bsj.2019.16.1.0130

Cisco Visual Networking Index: Forecast and Trends, 2017–2022. CISCO, 2019.

Cisco Annual Internet Report (2018–2023) White Paper. CISCO, 2020.

D. A. Abduljabbar, S. Z. M. Hashim, and R. Sallehuddin, "Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends," Telecommunication Systems, vol. 74, no. 2, pp. 225–252, Jun. 2020. DOI: https://doi.org/10.1007/s11235-019-00636-x

G. Beni and J. Wang, "Swarm Intelligence in Cellular Robotic Systems," in Robots and Biological Systems: Towards a New Bionics?, Toscana, Italy, Jun. 1989, pp. 703–712. DOI: https://doi.org/10.1007/978-3-642-58069-7_38

H. Yu, S. Wang, and Y. Bao, "Application of Ant Colony Algorithms in Path Planning of Ocean Survey," Journal of Coastal Research, vol. 94, no. SI, pp. 121–124, Jun. 2019. DOI: https://doi.org/10.2112/SI94-023.1

F. Ezazi, M. H. Mallah, J. Karimi Sabet, A. Norouzi, and A. Mahmoudian, "Investigation on the net cascade using Ant Colony optimization algorithm," Progress in Nuclear Energy, vol. 119, Jan. 2020, Art. no. 103169. DOI: https://doi.org/10.1016/j.pnucene.2019.103169

Md. R. Islam, C. M. K. Saifullah, and Md. R. Mahmud, "Chemical reaction optimization: survey on variants," Evolutionary Intelligence, vol. 12, no. 3, pp. 395–420, Sep. 2019. DOI: https://doi.org/10.1007/s12065-019-00246-1

M. A. Summakieh, C. K. Tan, A. A. El-Saleh, and T. C. Chuah, "Improved load balancing for LTE-A heterogeneous networks using particle swarm optimization," International Journal of Technology, vol. 10, no. 7, pp. 1407–1415, Nov. 2019. DOI: https://doi.org/10.14716/ijtech.v10i7.3253

Τ. M. Kumar and Ν. A. Singh, "Environmental Economic Dispatch with the use of Particle Swarm Optimization Technique based on Space Reduction Strategy," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4605–4611, Oct. 2019. DOI: https://doi.org/10.48084/etasr.2969

S. Aslan, H. Badem, and D. Karaboga, "Improved quick artificial bee colony (iqABC) algorithm for global optimization," Soft Computing, vol. 23, no. 24, pp. 13161–13182, Dec. 2019. DOI: https://doi.org/10.1007/s00500-019-03858-y

S. Kumari, P. K. Mishra, and V. Anand, "Integrated Load Balancing and Void Healing Routing with Cuckoo Search Optimization Scheme for Underwater Wireless Sensor Networks," Wireless Personal Communications, vol. 111, no. 3, pp. 1787–1803, Apr. 2020. DOI: https://doi.org/10.1007/s11277-019-06957-z

F. Safaei, H. Yeganloo, and R. Akbar, "Robustness on topology reconfiguration of complex networks: An entropic approach," Mathematics and Computers in Simulation, vol. 170, pp. 379–409, Apr. 2020. DOI: https://doi.org/10.1016/j.matcom.2019.11.013

G. R. Asha and Gowrishankar, "A hybrid approach for cost effective routing for WSNs using PSO and GSO algorithms," in International Conference on Big Data, IoT and Data Science, Pune, India, Dec. 2017, pp. 1–7. DOI: https://doi.org/10.1109/BID.2017.8336564

D. Rewadkar and D. Doye, "FGWSO-TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET," International Journal of Communication Systems, vol. 31, no. 1, 2018, Art. no. e3430. DOI: https://doi.org/10.1002/dac.3430

P. J and N. Venkataraman, "Threshold Based Multi-Objective Memetic Optimized Round Robin Scheduling for Resource Efficient Load Balancing in Cloud," Mobile Networks and Applications, vol. 24, no. 4, pp. 1214–1225, Aug. 2019. DOI: https://doi.org/10.1007/s11036-019-01259-x

K. Ono, T. Ono, K. Akimoto, S. Kameda, and N. Suematsu, "Traffic Navigation amp; Relay: System-Wide Load Balancing Method for Heterogeneous Network Using Route Direction and Packets Relay," in URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India, Mar. 2019, pp. 1–4. DOI: https://doi.org/10.23919/URSIAP-RASC.2019.8738647

S. Tahzeeb and S. Hasan, "A Neural Network-Based Multi-Label Classifier for Protein Function Prediction," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7974–7981, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4597

Y. Bella and F. Z. Kebbab, "Application of Fminsearch Optimization to Minimize Total Maintenance Cost with the Aim of Reducing Environmental Degradation," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8548–8554, Jun. 2022. DOI: https://doi.org/10.48084/etasr.4887

W. Lin, G. Peng, X. Bian, S. Xu, V. Chang, and Y. Li, "Scheduling Algorithms for Heterogeneous Cloud Environment: Main Resource Load Balancing Algorithm and Time Balancing Algorithm," Journal of Grid Computing, vol. 17, no. 4, pp. 699–726, Dec. 2019. DOI: https://doi.org/10.1007/s10723-019-09499-7

N. Yu, Z. Song, H. Du, H. Huang, and X. Jia, "Dynamic Resource Provisioning for Energy Efficient Cloud Radio Access Networks," IEEE Transactions on Cloud Computing, vol. 7, no. 4, pp. 964–974, Jul. 2019. DOI: https://doi.org/10.1109/TCC.2017.2715812

A. Sari and E. Caglar, "Load Balancing Algorithms and Protocols to Enhance Quality of Service and Performance in Data of WSN," in Security and Resilience in Intelligent Data-Centric Systems and Communication Networks, M. Ficco and F. Palmieri, Eds. Cambridge, MA, United States: Academic Press, 2018, pp. 143–178. DOI: https://doi.org/10.1016/B978-0-12-811373-8.00007-0

R. Khan, "An efficient load balancing and performance optimization scheme for constraint oriented networks," Simulation Modelling Practice and Theory, vol. 96, Nov. 2019, Art. no. 101930. DOI: https://doi.org/10.1016/j.simpat.2019.101930

R. Sharma, N. Mittal, and B. S. Sohi, "Flower pollination algorithm-based energy-efficient stable clustering approach for WSNs," International Journal of Communication Systems, vol. 33, no. 7, 2020, Art. no. e4337. DOI: https://doi.org/10.1002/dac.4337

Y. Zhang and Y. Wang, "A novel energy-aware bio-inspired clustering scheme for IoT communication," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 4239–4248, Oct. 2020. DOI: https://doi.org/10.1007/s12652-020-01704-w

P. M. Daflapurkar, M. Gandhi, and B. Patil, "Tree based distributed clustering routing scheme for energy efficiency in wireless sensor networks," in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, Sep. 2017, pp. 2450–2456. DOI: https://doi.org/10.1109/ICPCSI.2017.8392157

Y. Liao, H. Qi, and W. Li, "Load-Balanced Clustering Algorithm With Distributed Self-Organization for Wireless Sensor Networks," IEEE Sensors Journal, vol. 13, no. 5, pp. 1498–1506, Feb. 2013. DOI: https://doi.org/10.1109/JSEN.2012.2227704

A. Khan, F. Aftab, and Z. Zhang, "Self-organization based clustering scheme for FANETs using Glowworm Swarm Optimization," Physical Communication, vol. 36, Oct. 2019, Art. no. 100769. DOI: https://doi.org/10.1016/j.phycom.2019.100769

Y. Xiuwu, L. Qin, L. Yong, H. Mufang, Z. Ke, and X. Renrong, "Uneven clustering routing algorithm based on glowworm swarm optimization," Ad Hoc Networks, vol. 93, Oct. 2019, Art. no. 101923. DOI: https://doi.org/10.1016/j.adhoc.2019.101923

S. R. Salkuti and S.-C. Kim, "Congestion Management Using Multi-Objective Glowworm Swarm Optimization Algorithm," Journal of Electrical Engineering & Technology, vol. 14, no. 4, pp. 1565–1575, Jul. 2019. DOI: https://doi.org/10.1007/s42835-019-00206-w

A. N. Shinde, S. L. Lalbalwar, and A. B. Nandgaonkar, "Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals," International Journal of Wavelets, Multiresolution and Information Processing, vol. 17, no. 5, Sep. 2019, Art. no. 1950031. DOI: https://doi.org/10.1142/S0219691319500310

S. Barak, V. J. Kompella, K. N. Kaipa, and D. Ghose, "Push-pull glowworm swarm optimization algorithm for multimodal functions," in Swarm Intelligence -Volume 2: Innovation, new algorithms and methods, IET, 2018, pp. 185–219. DOI: https://doi.org/10.1049/PBCE119G_ch7

G. Miryala and S. A. Ludwig, "Comparing Spark with MapReduce: Glowworm Swarm Optimization Applied to Multimodal Functions," International Journal of Swarm Intelligence Research, vol. 9, no. 3, pp. 1–22, Jul. 2018. DOI: https://doi.org/10.4018/IJSIR.2018070101

P. Qiong, Y. Liao, P. Hao, X. He, and C. Hui, "A Self-Adaptive Step Glowworm Swarm Optimization Approach," International Journal of Computational Intelligence and Applications, vol. 18, no. 1, Mar. 2019, Art. no. 1950004. DOI: https://doi.org/10.1142/S1469026819500044

R. Isimeto, C. Yinka-Banjo, C. O. Uwadia, and D. C. Alienyi, "An enhanced clustering analysis based on glowworm swarm optimization," in 4th International Conference on Soft Computing & Machine Intelligence, Mauritius, Nov. 2017, pp. 42–49. DOI: https://doi.org/10.1109/ISCMI.2017.8279595

Y. Verma, "Secure system simulation - Internet of Things," Ph.D. dissertation, California State University, California, USA, 2016.


How to Cite

T. Akhtar, N. G. Haider, and S. M. Khan, “A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 4, pp. 8777–8784, Aug. 2022.


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