Pseudo Gradient Teaching Learning Based Optimization for Proactive SLA Prediction in 6G Networks
DOI:
https://doi.org/10.4108/eetiot.13675Keywords:
teaching learning based optimization, pseudo gradient, metaheuristic, neural network training, 6G networks, service level agreement prediction, mobile edge computingAbstract
Proactive prediction of Service Level Agreement (SLA) satisfaction lets 6G radio access networks act before a link misses its throughput and latency targets. This paper proposes Pseudo Gradient Teaching Learning Based Optimization (PGTLBO), which adds to the teacher step the direction from the worse half to the better half of the ranked population, and uses it to train a multilayer perceptron. All trainers receive the same number of function evaluations, and a random direction control isolates the effect of the direction. On 6G SLA prediction for unseen clients, PGTLBO reaches 0.846 accuracy, tied with standard TLBO (0.844), and outperforms the genetic algorithm, particle swarm optimization, and Harris hawks optimization on all 20 splits. PGTLBO reaches a lower training loss than TLBO in 57 of 80 paired runs and its direction beats a random one, but test accuracy is limited by the data.
Downloads
References
[1] Goldberg DE. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison Wesley; 1989.
[2] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks; 1995. p. 1942-8.
[3] Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: algorithm and applications. Future Generation Computer Systems. 2019;97:849-72.
[4] Ojha VK, Abraham A, Snášel V. Metaheuristic design of feedforward neural networks: a review of two decades of research. Engineering Applications of Artificial Intelligence. 2017;60:97-116.
[5] Nuha HH, Ariyanto E, Nurma Yulita II, Sailellah HRP. Clustered packet optimization for human powered energy generation prediction. Discover Internet of Things. 2026;6(1):111.
[6] Fauzan MN, Munadi R, Sumaryo S, Nuha HH. Enhanced wireless sensor network lifetime using EGWO optimized neural network approach. Network. 2026;6(1):5.
[7] Al-Shaikhi A, Nuha H, Mohandes M, Rehman S, Adrian M. Vertical wind speed extrapolation model using long short term memory and particle swarm optimization. Energy Science & Engineering. 2022;10(12):4580-94.
[8] Fauzan MN, Munadi R, Sumaryo S, Nuha HH. Enhanced grey wolf optimization for efficient transmission power optimization in wireless sensor network. Applied System Innovation. 2025;8(2):36.
[9] Suyanto S, Ariyanto AA, Ariyanto AF. Komodo mlipir algorithm. Applied Soft Computing. 2022;114:108043.
[10] Setiawan D, Suyanto S, Erfianto B, Gozali AA. Battlefield optimization algorithm. Expert Systems with Applications. 2025;266:126097.
[11] Rao RV, Savsani VJ, Vakharia DP. Teaching learning based optimization: a novel method for constrained mechanical design optimization problems. Computer Aided Design. 2011;43(3):303-15.
[12] Hansen N, Ostermeier A. Completely derandomized self adaptation in evolution strategies. Evolutionary Computation. 2001;9(2):159-95.
[13] Wierstra D, Schaul T, Glasmachers T, Sun Y, Peters J, Schmidhuber J. Natural evolution strategies. Journal of Machine Learning Research. 2014;15:949-80. Available from: https://jmlr.org/papers/v15/wierstra14a.html.
[14] Gong W, Cai Z. Differential evolution with ranking based mutation operators. IEEE Transactions on Cybernetics. 2013;43(6):2066-81.
[15] Zhang Y, Jin Z. Group teaching optimization algorithm: a novel metaheuristic method for solving global optimization problems. Expert Systems with Applications. 2020;148:113246.
[16] Črepinšek M, Liu SH, Mernik L. A note on teaching learning based optimization algorithm. Information Sciences. 2012;212:79-93.
[17] Salimans T, Ho J, Chen X, Sidor S, Sutskever I. Evolution strategies as a scalable alternative to reinforcement learning; 2017. arXiv preprint arXiv:1703.03864. Available from: https://arxiv.org/abs/1703.03864.
[18] Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J. Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning; 2017. arXiv preprint arXiv:1712.06567. Available from: https://arxiv.org/abs/1712.06567.
[19] Kingma DP, Ba J. Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR); 2015. Available from: https://arxiv.org/abs/1412.6980.
[20] Hartono R, Mustika IW, Sulistyo S. Metaheuristic approaches for energy optimization in wireless sensor networks: a systematic review of trends, challenges, and future directions. EAI Endorsed Transactions on Internet of Things. 2026;11.
[21] Sabat NR, Sahoo RR, Acharya B, Kumar R. Hybrid genetic algorithm and water wave optimization approach for QoS aware multi objective task scheduling and load balancing in cloud environments. EAI Endorsed Transactions on Internet of Things. 2026;11.
[22] Yang Q, Chen B, Huang Y, Hu X. Optimal scheduling of renewable power distribution systems combining deep learning and particle swarm optimization. EAI Endorsed Transactions on Energy Web. 2026;13.
[23] Tran DT, Ha DB, Ansere JA. Overview of quantum machine learning for 6G. EAI Endorsed Transactions on Tourism, Technology and Intelligence. 2025;2(3).
[24] Rao RV. Teaching Learning Based Optimization Algorithm and Its Engineering Applications. Cham: Springer; 2016.
[25] Zou F, Chen D, Xu Q. A survey of teaching learning based optimization. Neurocomputing. 2019;335:366-83.
[26] Rao RV, Patel V. An elitist teaching learning based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations. 2012;3(4):535-60.
[27] Satapathy SC, Naik A. Modified teaching learning based optimization algorithm for global numerical optimization: a comparative study. Swarm and Evolutionary Computation. 2014;16:28-37.
[28] Zou F, Wang L, Hei X, Chen D, Yang D. Teaching learning based optimization with dynamic group strategy for global optimization. Information Sciences. 2014;273:112-31.
[29] Rao RV. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations. 2016;7(1):19-34.
[30] Jastrebski GA, Arnold DV. Improving evolution strategies through active covariance matrix adaptation. In: Proceedings of the IEEE Congress on Evolutionary Computation; 2006. p. 2814-21.
[31] Rubinstein R. The cross entropy method for combinatorial and continuous optimization. Methodology and Computing in Applied Probability. 1999;1(2):127-90.
[32] Larrañaga P, Lozano JA, editors. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Boston, MA: Kluwer Academic Publishers; 2002.
[33] Zhang J, Sanderson AC. JADE: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation. 2009;13(5):945-58.
[34] Tanabe R, Fukunaga A. Success history based parameter adaptation for differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation; 2013. p. 71-8.
[35] Storn R, Price K. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization. 1997;11(4):341-59.
[36] Cheng R, Jin Y. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics. 2015;45(2):191-204.
[37] Cheng R, Jin Y. A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences. 2015;291:43-60.
[38] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in Engineering Software. 2014;69:46-61.
[39] Ahmadianfar I, Bozorg-Haddad O, Chu X. Gradient based optimizer: a new metaheuristic optimization algorithm. Information Sciences. 2020;540:131-59.
[40] Mao Y, You C, Zhang J, Huang K, Letaief KB. A survey on mobile edge computing: the communication perspective. IEEE Communications Surveys and Tutorials. 2017;19(4):2322-58.
[41] Maitlo N, Shah MH, Maitlo A, Mustafa G, Arshid K, Noonari N. Communication fairness trade offs in federated learning for 6G resource allocation: a 200 client study. Inventions. 2026;11(2):31.
[42] Maitlo N, Rajper S, Shaikh M, Kareem H. Robust privacy preserving federated learning for 6G network resource allocation. ILMA Journal of Technology & Software Management. 2026;7(1):1-8. Available from: https://ijtsm.ilmauniversity.edu.pk/arc/Vol7/i1P1.
[43] UCI Machine Learning. Task Offloading Dataset; 2023. Kaggle. Available from: https://www.http://kaggle.com/datasets/ucimachinelearning/task-offloading-dataset.
[44] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research. 2011;12:2825-30. Available from: https://jmlr.org/papers/v12/pedregosa11a.html.
[45] Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research. 2006;7:1-30. Available from: https://jmlr.org/papers/v7/demsar06a.html.
[46] Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics. 1979;6(2):65-70. Available from: https://www.jstor.org/stable/4615733.
[47] Derrac J, García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation. 2011;1(1):3-18.
[48] Vargha A, Delaney HD. A critique and improvement of the CL common language effect size statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics. 2000;25(2):101-32.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Hilal Hudan Nuha, Pratama Azmi Atmajaya, Gian Maxmillian Firdaus
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
