Application of Slime Mould Algorithm to Infinite Impulse Response System Identification Problem

Abstract

Recently, the researchers working in the field of science and engineering have paid a considerable attention to the concept of the system identification to tackle with complex optimization problems. It is feasible to achieve more accurate models of physical plants with the infinite impulse response (IIR) models compared to their finite counterparts (FIR). To get the most out of the IIR models for the system identification, metaheuristic optimization algorithms can be used as efficient solutions. This work, therefore, aims to demonstrate more promising performance of a new metaheuristic algorithm named slime mould algorithm. In this regard, a comparative assessment is performed using different metaheuristic optimization techniques and different IIR model identification problems are considered. The slime mould algorithm is shown to achieve better accuracy and robustness in terms of IIR model identification with the help of obtained statistical results.

Keywords

• IIR adaptive filter
• System identification
• Slime mould algorithm
• Metaheuristic algorithms

References

1. Cuevas, E., Gálvez, J., Hinojosa, S., Avalos, O., Zaldívar, D., & Pérez-Cisneros, M. (2014). A Comparison of Evolutionary Computation Techniques for IIR Model Identification. Journal of Applied Mathematics, 2014, 1–9. https://doi.org/10.1155/2014/827206
2. Durmuş, B. (2022). Infinite impulse response system identification using average differential evolution algorithm with local search. Neural Computing and Applications, 34(1), 375–390. https://doi.org/10.1007/s00521-021-06399-4
3. Ekinci, S., Izci, D., Zeynelgil, H. L., & Orenc, S. (2020). An Application of Slime Mould Algorithm for Optimizing Parameters of Power System Stabilizer. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 1–5. https://doi.org/10.1109/ISMSIT50672.2020.9254597
4. Eswari, P., Ramalakshmanna, Y., & Durga Prasad, C. (2021). An Improved Particle Swarm Optimization-Based System Identification (pp. 137–142). https://doi.org/10.1007/978-981-16-0289-4_11
5. Izci, D. (2021). An Enhanced Slime Mould Algorithm for Function optimization. 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 1–5. https://doi.org/10.1109/HORA52670.2021.9461325
6. Izci, D., & Ekinci, S. (2021). Comparative Performance Analysis of Slime Mould Algorithm For Efficient Design of Proportional–Integral–Derivative Controller. Electrica, 21(1), 151–159. https://doi.org/10.5152/electrica.2021.20077
7. Izci, D., Ekinci, S., Eker, E., & Dundar, A. (2021). Assessment of Slime Mould Algorithm Based Real PID Plus Second-order Derivative Controller for Magnetic Levitation System. 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 6–10. https://doi.org/10.1109/ISMSIT52890.2021.9604620
8. Izci, D., Ekinci, S., Zeynelgil, H. L., & Hedley, J. (2021). Fractional Order PID Design based on Novel Improved Slime Mould Algorithm. Electric Power Components and Systems, 49(9–10), 901–918. https://doi.org/10.1080/15325008.2022.2049650

9. Kamboj, V. K., Kumari, C. L., Bath, S. K., Prashar, D., Rashid, M., Alshamrani, S. S., & AlGhamdi, A. S. (2022). A Cost-Effective Solution for Non-Convex Economic Load Dispatch Problems in Power Systems Using Slime Mould Algorithm. Sustainability, 14(5), 2586. https://doi.org/10.3390/su14052586
10. Karaboga, N. (2009). A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 346(4), 328–348. https://doi.org/10.1016/j.jfranklin.2008.11.003
11. Kumar, M., Aggarwal, A., & Rawat, T. K. (2016). Bat Algorithm: Application to Adaptive Infinite Impulse Response System Identification. Arabian Journal for Science and Engineering, 41(9), 3587–3604. https://doi.org/10.1007/s13369-016-2222-3
12. Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055
13. Mohammadi, A., Zahiri, S. H., & Razavi, S. M. (2019). Infinite impulse response systems modeling by artificial intelligent optimization methods. Evolving Systems, 10(2), 221–237. https://doi.org/10.1007/s12530-018-9218-z
14. Mostafa, M., Rezk, H., Aly, M., & Ahmed, E. M. (2020). A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel. Sustainable Energy Technologies and Assessments, 42, 100849. https://doi.org/https://doi.org/10.1016/j.seta.2020.100849
15. Panda, G., Pradhan, P. M., & Majhi, B. (2011). IIR system identification using cat swarm optimization. Expert Systems with Applications, 38(10), 12671–12683. https://doi.org/10.1016/j.eswa.2011.04.054
16. Saha, S. K., Kar, R., Mandal, D., & Ghoshal, S. P. (2014). Harmony search algorithm for infinite impulse response system identification. Computers & Electrical Engineering, 40(4), 1265–1285. https://doi.org/10.1016/j.compeleceng.2013.12.016
17. Tiachacht, S., Khatir, S., Thanh, C. Le, Rao, R. V., Mirjalili, S., & Abdel Wahab, M. (2021). Inverse problem for dynamic structural health monitoring based on slime mould algorithm. Engineering with Computers. https://doi.org/10.1007/s00366-021-01378-8
18. Zhao, R., Wang, Y., Liu, C., Hu, P., Jelodar, H., Yuan, C., Li, Y., Masood, I., Rabbani, M., Li, H., & Li, B. (2020). Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem. Soft Computing, 24(10), 7637–7684. https://doi.org/10.1007/s00500-019-04390-9