Rasler hyper chaotic system identification using improved Moth-flame Optimization Algorithm with Tabu Search

Subject Areas : Electrical engineering (electronics, telecommunications, power, control)

Mostafa Saadatifar ¹ ( )
Mahsa Esmaeilnia ² ( )
Mahdi Yaghoobi ³ ( Computer Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran. )

Received: 2024-05-25 Accepted : 2024-08-27 Published : 2024-09-21

Keywords: hyper chaotic system, Moth-flame Optimization Algorithm, Tabu Search,

Abstract :

In this article, the problem of identifying chaotic systems with the help of butterfly flame algorithm improved with forbidden search algorithm is discussed. The problem of identifying chaotic systems is a problem with many local optima. For this purpose, a powerful optimization algorithm is needed to solve it. The flame-butterfly algorithm, which is inspired by the spiral movement of the butterfly around the candle, has several features, including the balance between exploration and mining. But in terms of local search, it is weak and needs improvement. In this article, in order to improve this algorithm, the forbidden search method is used in combination for the first time. The goal is to improve the extraction ability and avoid getting trapped in the local optimum in the butterfly flame algorithm. In this article, the problem of identifying chaotic systems with the help of butterfly flame algorithm improved with forbidden search algorithm is discussed. The problem of identifying chaotic systems is a problem with many local optima. For this purpose, a powerful optimization algorithm is needed to solve it. The flame-butterfly algorithm, which is inspired by the spiral movement of the butterfly around the candle, has several features, including the balance between exploration and mining. But in terms of local search, it is weak and needs improvement. In this article, in order to improve this algorithm, the forbidden search method is used in combination for the first time. The goal is to improve the extraction ability and avoid getting trapped in the local optimum in the butterfly flame algorithm.

References:

[1] Chen, G. and Dong, X., 1999. From chaos to order: Methodologies prespectives and applications world scientific Singapore 1998. In Handbook of Chaos Control. Weinheim.
[2] Chang, W.D., Cheng, J.P., Hsu, M.C. and Tsai, L.C., 2012, December. Parameter identification of nonlinear systems using a particle swarm optimization approach. In 2012 Third International Conference on Networking and Computing (pp. 113-117). IEEE.
[3] Xiang-Tao, L. and Ming-Hao, Y., 2012. Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method. Chinese Physics B, 21(5), p.050507.
[4] Li, X. and Yin, M., 2014. Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dynamics, 77(1), pp.61-71.
[5] Ko, C.N., Jau, Y.M. and Jeng, J.T., 2015. Parameter Estimation of Chaotic Dynamical Systems Using HEQPSO. J. Inf. Sci. Eng., 31(2), pp.675-689.
[6] Zhang, H., Li, B., Zhang, J., Qin, Y., Feng, X. and Liu, B., 2016. Parameter estimation of nonlinear chaotic system by improved TLBO strategy. Soft Computing, 20(12), pp.4965-4980.
[7] Wang, J., Zhou, B. and Zhou, S., 2016. An improved cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation. Computational intelligence and neuroscience, 2016.
[8] Li, H. and Wu, H., 2016. An oppositional wolf pack algorithm for parameter identification of the chaotic systems. Optik, 127(20), pp.9853-9864.
[9] Lazzús, J. A., Rivera, M., & López-Caraballo, C. H. (2016). Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm. Physics Letters A, 380(11-12), 1164-1171.
[10] Rahimi, A., Bavafa, F., Aghababaei, S., Khooban, M.H. and Naghavi, S.V., 2016. The online parameter identification of chaotic behaviour in permanent magnet synchronous motor by self-adaptive learning bat-inspired algorithm. International Journal of Electrical Power & Energy Systems, 78, pp.285-291.
[11] Ding, Z., Lu, Z. and Liu, J., 2018. Parameters identification of chaotic systems based on artificial bee colony algorithm combined with cuckoo search strategy. Science China Technological Sciences, 61(3), pp.417-426.
[12] Xu, S., Wang, Y. and Liu, X., 2018. Parameter estimation for chaotic systems via a hybrid flower pollination algorithm. Neural Computing and Applications, 30(8), pp.2607-2623.
[13] Wei, J. and Yu, Y., 2017. An effective hybrid cuckoo search algorithm for unknown parameters and time delays estimation of chaotic systems. IEEE Access, 6, pp.6560-6571.
[14] Mousavi, Y. and Alfi, A., 2018. Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems. Chaos, Solitons & Fractals, 114, pp.202-215.
[15] Sheludko, A.S., 2018. Parameter estimation for one-dimensional chaotic systems by guaranteed algorithm and particle swarm optimization. IFAC-PapersOnLine, 51(32), pp.337-342.
[16] Shekofteh, Y., Panahi, S., Boubaker, O. and Jafari, S., 2019. Parameter Estimation of Chaotic Systems Using Density Estimation of Strange Attractors in the State Space. In Recent Advances in Chaotic Systems and Synchronization (pp. 105-124). Academic Press.
[17] Chen, Y., Pi, D. and Wang, B., 2019. Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system. Nonlinear Dynamics, 97(2), pp.1343-1358.
[18] Yousri, D., Allam, D. and Eteiba, M.B., 2019. Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in Permanent Magnet Synchronous Motor. Applied Soft Computing, 74, pp.479-503.
[19] Ebrahimi, S.M., Malekzadeh, M., Alizadeh, M. and HosseinNia, S.H., 2019. Parameter identification of nonlinear system using an improved Lozi map based chaotic optimization algorithm (ILCOA). Evolving Systems, pp.1-18.
[20] Mousazadeh, A. and Shekofteh, Y., 2020. Cost function based on the self-organizing map for parameter estimation of chaotic discrete-time systems. Engineering Applications of Artificial Intelligence, 94, p.103817.
[21] Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, pp.228-249.
[22] Glover, F. and Laguna, M., 1998. Tabu search. In Handbook of combinatorial optimization (pp. 2093-2229). Springer, Boston, MA.
[23] Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2013). Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635, 490