A comparison of evolutionary computation techniques for IIR model identification. (English) Zbl 1463.93047

Summary: System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.


93B30 System identification
93C27 Impulsive control/observation systems
90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI


[1] Zhou, X.; Yang, C.; Gui, W., Nonlinear system identification and control using state transition algorithm, Applied Mathematics and Computation, 226, 169-179 (2014) · Zbl 1354.93148
[2] Albaghdadi, M.; Briley, B.; Evens, M., Event storm detection and identification in communication systems, Reliability Engineering and System Safety, 91, 5, 602-613 (2006)
[3] Frank Pai, P.; Nguyen, B.-A.; Sundaresan, M. J., Nonlinearity identification by time-domain-only signal processing, International Journal of Non-Linear Mechanics, 54, 85-98 (2013)
[4] Chung, H.-C.; Liang, J.; Kushiyama, S.; Shinozuka, M., Digital image processing for non-linear system identification, International Journal of Non-Linear Mechanics, 39, 5, 691-707 (2004) · Zbl 1348.94005
[5] Na, J.; Ren, X.; Xia, Y., Adaptive parameter identification of linear SISO systems with unknown time-delay, Systems & Control Letters, 66, 1, 43-50 (2014) · Zbl 1288.93089
[6] Kukrer, O., Analysis of the dynamics of a memoryless nonlinear gradient IIR adaptive notch filter, Signal Processing, 91, 10, 2379-2394 (2011) · Zbl 1219.94035
[7] Mostajabi, T.; Poshtan, J.; Mostajabi, Z., IIR model identification via evolutionary algorithms—a comparative study, Artificial Intelligence Review (2013)
[8] Dai, C.; Chen, W.; Zhu, Y., Seeker optimization algorithm for digital IIR filter design, IEEE Transactions on Industrial Electronics, 57, 5, 1710-1718 (2010)
[9] Fang, W.; Sun, J.; Xu, W., A new mutated quantum-behaved particle swarm optimizer for digital IIR filter design, Eurasip Journal on Advances in Signal Processing, 2009 (2009) · Zbl 1192.94042
[10] Kennedy, J.; Eberhart, R. C., Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks
[11] Karaboga, D., An idea based on honey bee swarm for numerical optimization, TR06 (2005), Computer Engineering Department, Engineering Faculty, Erciyes University
[12] İlker, B.; Birbil, S.; Shu-Cherng, F., An electromagnetism-like mechanism for global optimization, Journal of Global Optimization, 25, 3, 263-282 (2003) · Zbl 1047.90045
[13] Yang, X.-S.; Deb, S., Cuckoo search via Lévy flights, Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC ’09)
[14] Yang, X. S., Flower pollination algorithm for global optimization, Unconventional Computation and Natural Computation. Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, 7445, 240-249 (2012), Berlin, Germany: Springer, Berlin, Germany · Zbl 1374.68527
[15] Ahn, C., Advances in Evolutionary Algorithms: Theory, Design and Practice (2006), New York, NY, USA: Springer, New York, NY, USA · Zbl 1103.68105
[16] Chiong, R.; Weise, T.; Michalewicz, Z., Variants of Evolutionary Algorithms for Real-World Applications (2012), New York, NY, USA: Springer, New York, NY, USA
[17] Oltean, M., Evolving evolutionary algorithms with patterns, Soft Computing, 11, 6, 503-518 (2007)
[18] Chen, S.; Luk, B. L., Digital IIR filter design using particle swarm optimisation, International Journal of Modelling, Identification and Control, 9, 4, 327-335 (2010)
[19] Karaboga, N., A new design method based on artificial bee colony algorithm for digital IIR filters, Journal of the Franklin Institute, 346, 4, 328-348 (2009) · Zbl 1166.93351
[20] Cuevas, E.; Oliva, D., IIR filter modeling using an algorithm inspired on electromagnetism, Ingeniería Investigación y Tecnología, 14, 1, 125-138 (2013)
[21] Patwardhan, A. P.; Patidar, R.; George, N. V., On a cuckoo search optimization approach towards feedback system identification, Digital Signal Processing, 32, 156-163 (2014)
[22] Wolpert, D. H.; Macready, W. G., No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1, 1, 67-82 (1997)
[23] Elbeltagi, E.; Hegazy, T.; Grierson, D., Comparison among five evolutionary-based optimization algorithms, Advanced Engineering Informatics, 19, 1, 43-53 (2005)
[24] Shilane, D.; Martikainen, J.; Dudoit, S.; Ovaska, S. J., A general framework for statistical performance comparison of evolutionary computation algorithms, Information Sciences, 178, 14, 2870-2879 (2008)
[25] Osuna-Enciso, V.; Cuevas, E.; Sossa, H., A comparison of nature inspired algorithms for multi-threshold image segmentation, Expert Systems with Applications, 40, 4, 1213-1219 (2013)
[26] Lin, Y.-L.; Chang, W.-D.; Hsieh, J.-G., A particle swarm optimization approach to nonlinear rational filter modeling, Expert Systems with Applications, 34, 2, 1194-1199 (2008)
[27] Pavlyukevich, I., Lévy flights, non-local search and simulated annealing, Journal of Computational Physics, 226, 2, 1830-1844 (2007) · Zbl 1125.65009
[28] Mantegna, R. N., Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes, Physical Review E, 49, 5, 4677-4683 (2007)
[29] Cuevas, E.; González, M.; Zaldivar, D.; Pérez-Cisneros, M.; García, G., An algorithm for global optimization inspired by collective animal behavior, Discrete Dynamics in Nature and Society, 2012 (2012)
[30] Cuevas, E.; Cienfuegos, M.; Zaldívar, D.; Pérez-Cisneros, M., A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40, 16, 6374-6384 (2013)
[31] García, S.; Molina, D.; Lozano, M.; Herrera, F., A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization, Journal of Heuristics, 15, 6, 617-644 (2009) · Zbl 1191.68828
[32] Shilane, D.; Martikainen, J.; Dudoit, S.; Ovaska, S. J., A general framework for statistical performance comparison of evolutionary computation algorithms, Information Sciences, 178, 14, 2870-2879 (2008)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.