An Improved Electromagnetism-like Algorithm for Gaussian Process Hyperparameter Optimization in Remaining Useful Life Prediction of Electrolytic Capacitors
DOI:
https://doi.org/10.64470/elene.2026.35Keywords:
Gaussian Process Regression (GPR), lectromagnetism-like Algorithm, Electrolytic Capacitors, Negative Log Marginal Likelihood, Remaining Useful LifeAbstract
This paper introduces an innovative optimization procedure to enhance gaussian process regression (GPR) for hyperparameter tuning. The technique estimates the remaining useful life (RUL) of electrolytic capacitors under electrical overstress conditions. The traditional electromagnetism-like (EM) optimization technique suffers premature convergence and insufficient exploitation near local optimal solutions. It also suffers ineffective charge relocation within high-dimensional search spaces. To address these limitations, the adaptive chaotic opposition-based learning electromagnetism-like (ACOEM) algorithm is proposed. The new algorithm incorporates three techniques to overcome these challenges: opposition-based learning to increase initial population size and speed up the optimization process; chaotic map-based scaling of forces to strike a balance between exploration and exploitation; and lévy flights to avoid local minima. The ACOEM optimizer fine-tunes the hyperparameters of the GPR kernel by minimizing the negative log marginal likelihood (NLML) objective function. The proposed ACOEM-GPR predictor is successfully applied to National Aeronautics and Space Administration (NASA’s) electrochemical capacitor electrical overstress dataset based on three (3) stress levels: 10V, 12V and 14V. Compared to the vanilla GPR predictor, ACOEM-GPR exhibited superior performance in terms of the correlation coefficient , improving from 0.3015 to 0.7482 for 10V stress and from 0.5615 to 0.8921 for 14V stress. Additionally, the ACOEM-GPR method showed significant improvements in mean squared error (MSE) reducing the MSE from 9458 to 3410 and root mean squared error (RMSE) from 97.3 to 58.4 at 10V. ACOEM-GPR is proven to produce accurate and robust RUL predictions allowing for effective predictive maintenance of power converter capacitors.
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Asigri, P., Frimpong, E. A., Anto, E. K., Kwegyir, D., & Effah, F. B. (2024). Enhanced Multi-Objective Grey Wolf Optimization using Adaptive Diversity Tuning and Levy Flights. Carpathian Journal of Electrical Engineering, 7–35. https://doi.org/10.34302/cjee/tkyd3692
Birbil, Ş. İ., & Fang, S.-C. (2003). An Electromagnetism-like Mechanism for Global Optimization. Journal of Global Optimization, 25(3), 263–282. https://doi.org/10.1023/A:1022452626305
Dai, X., Long, Z., & Zhang, J. (2015). PSO based on chaotic map and its application to PID controller self-tuning. 2015 16th International Conference on Electronic Packaging Technology (ICEPT), 1470–1476. https://doi.org/10.1109/ICEPT.2015.7236860
TDK Electronics Co., L. (2026). Electrolytic Capacitors (Part 7, Vol. 2): Key Features - TDK｜Electronics ABC｜Learn about Technology with TDK. https://www.tdk.com/en/tech-mag/electronics_primer/10
Forouzandeh Shahraki, Ameneh, Al-Dahidi, Sameer, Rahim Taleqani, Ali, & Yadav, Om Prakash. (2023). Using LSTM neural network to predict remaining useful life of electrolytic capacitors in dynamic operating conditions. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 237(1), 16–28. https://doi.org/10.1177/1748006X221087503
Jha, B., & Dong, L. (2024). Lifetime Improvement With Predictive Maintenance of Power Electronics Based on Remaining Useful Life Prediction. 2024 IEEE Texas Power and Energy Conference, TPEC 2024. https://doi.org/10.1109/TPEC60005.2024.10472254
Khorasgani, H., Kulkarni, C., Biswas, G., Celaya, J. R., & Goebel, K. (2013). Degredation Modeling and Remaining Useful Life Prediction of Electrolytic Capacitors under Thermal Overstress Condition Using Particle Filters. https://doi.org/https://doi.org/10.36001/phmconf.2013.v5i1.2277
Kulevome, D. K. B., Wang, H., & Wang, X. (2021). A Bidirectional LSTM-Based Prognostication of Electrolytic Capacitor. In Progress In Electromagnetics Research C (Vol. 109). https://doi.org/http://dx.doi.org/10.2528/PIERC20120201
Kullampalayam Murugaiyan, N., Chandrasekaran, K., Manoharan, P., & Derebew, B. (2024). Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-023-50890-y
Lin, J.-L., Wu, C.-H., & Chung, H.-Y. (2012). Performance Comparison of Electromagnetism-Like Algorithms for Global Optimization. Applied Mathematics, 03(10), 1265–1275. https://doi.org/10.4236/am.2012.330183
Long, W., Jiao, J., Liang, X., Cai, S., & Xu, M. (2019). A Random Opposition-Based Learning Grey Wolf Optimizer. IEEE Access, 7, 113810–113825. https://doi.org/10.1109/ACCESS.2019.2934994
Oliva, D., Cuevas, E., Pajares, G., & Zaldivar, D. (2014). Template matching using an improved electromagnetism-like algorithm. Applied Intelligence, 41(3), 791–807. https://doi.org/10.1007/s10489-014-0552-y
Ragb, O., Bakr, H., & Civalek, O. (2023). Parameters identification for photovoltaic system via improved electromagnetism-like approach and quadrature technique. International Journal of Energy and Environmental Engineering, 14(3), 353–377. https://doi.org/10.1007/s40095-022-00523-3
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2006). Opposition-Based Differential Evolution for Optimization of Noisy Problems. 2006 IEEE International Conference on Evolutionary Computation, 1865–1872. https://doi.org/10.1109/CEC.2006.1688534
Renwick, J., Kulkarni, C. S., & Celaya, J. R. (2015). Analysis of electrolytic capacitor degradation under electrical overstress for prognostic studies. Vol. 7 No. 1 (2015): Proceedings of the Annual Conference of the PHM Society 2015, 6. https://doi.org/https://doi.org/10.36001/phmconf.2015.v7i1.2713
Rigamonti, M., Baraldi, P., Zio, E., Astigarraga, D., & Galarza, A. (2016). Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions. IEEE Transactions on Power Electronics, 31(2), 1567–1575. https://doi.org/10.1109/TPEL.2015.2418198
Roman, D., Saxena, S., Bruns, J., Valentin, R., Pecht, M., & Flynn, D. (2021). A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature. IEEE Access, 9, 25544–25553. https://doi.org/10.1109/ACCESS.2021.3057959
Tan, J. D., Dahari, M., Koh, S. P., Koay, Y. Y., & Abed, I. A. (2016). An improved electromagnetism-like algorithm for numerical optimization. Theoretical Computer Science, 641, 75–84. https://doi.org/10.1016/j.tcs.2016.05.045
Wang, X., Jiang, B., Wu, S., Lu, N., & Ding, S. X. (2022). Multivariate Relevance Vector Regression Based Degradation Modeling and Remaining Useful Life Prediction. IEEE Transactions on Industrial Electronics, 69(9), 9514–9523. https://doi.org/10.1109/TIE.2021.3114724
Wang, Z., Chen, Y., Ding, S., Liang, D., & He, H. (2022). A novel particle swarm optimization algorithm with Lévy flight and orthogonal learning. Swarm and Evolutionary Computation, 75, 101207. https://doi.org/https://doi.org/10.1016/j.swevo.2022.101207
Yang, X.-S., & Deb, S. (2010). Engineering Optimisation by Cuckoo Search. https://doi.org/https://doi.org/10.48550/arXiv.1005.2908
Yu, F., Guan, J., Wu, H., Chen, Y., & Xia, X. (2024). Lens imaging opposition-based learning for differential evolution with cauchy perturbation. Applied Soft Computing, 152, 111211. https://doi.org/https://doi.org/10.1016/j.asoc.2023.111211
Zhang, B., Liu, W., Cai, Y., Zhou, Z., Wang, L., Liao, Q., Fu, Z., & Cheng, Z. (2024). State of health prediction of lithium-ion batteries using particle swarm optimization with Levy flight and generalized opposition-based learning. Journal of Energy Storage, 84, 110816. https://doi.org/https://doi.org/10.1016/j.est.2024.110816
Zhang, Y., Zhang, C., & Cheng, Z. (2022). Parameter identification based on chaotic map simulated annealing genetic algorithm for PMSWG. Prog. Electromagn. Res. M, 113, 59–71. https://doi.org/doi:10.2528/PIERM22070101
Zhu, C., Zhang, Y., Wang, M., Deng, J., Cai, Y., Wei, W., & Guo, M. (2024). Optimization, validation and analyses of a hybrid PV-battery-diesel power system using enhanced electromagnetic field optimization algorithm and ε-constraint. Energy Reports, 11, 5335–5349. https://doi.org/10.1016/j.egyr.2024.04.043
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Data Availability Statement
Data for this research can be accessed from NASA's Prognostics Center of Excellence Dataset Repository.
Link: https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
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Copyright (c) 2026 Kofi Addo Annan, Emmanuel Assuming Frimpong, Francis Boafo Effah, Elvis Twumasi, Abdul-Majid Issah Malori, Mark Ampomah Otu
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