Abstract
This paper presents a novel approach for the design of two-dimensional (2D) Finite Impulse Response (FIR) filters. The design of FIR filters is generally non-differentiable, multimodal and higher dimensional; especially for 2D filters. A large number of filter coefficients are optimized either using constrained or unconstrained optimization approach. Due to the large number of constraints, traditional design methods cannot produce optimal filters required for some crucial applications. This makes meta-heuristic algorithms as good alternatives for addressing such constraints more efficiently. In order to improve the performance of 2D filters, we propose an Accelerated Artificial Bee Colony algorithm, termed as AABC algorithm. The earlier reported ABC based methods perform the modification of a single parameter of the solution in each cycle. But in this proposed AABC algorithm, we have adopted multiple parameters change of search equation at each step. This in turn improves the convergence speed of the algorithm by three times than the classical ABC algorithm and two times with respect to recently developed CABC method. In order to achieve better exploration behaviour of abandoned bees, we have also introduced a change during the initialization strategy of scout bees in the proposed AABC algorithm. The efficiency and robustness of the proposed algorithm are demonstrated by comparing its performance with classical Genetic Algorithm (GA), Particle Swarm Optimization , ABC and CABC methods.
Similar content being viewed by others
References
Abatzoglou, T. J., & Jaffer, A. G. (1995). Least pth power design of complex FIR 2-D filters using the complex Newton method. In Proceedings International Conference on Acoustics, Speech, and Signal Processing, 2, 1280–1283.
Alizadegan, A., Asady, B., & Ahmadpour, M. (2013). Two modified versions of artificial bee colony algorithm. Applied Mathematics and Computation, 225(1), 601–609.
Boudjelaba, K., Ros, F., & Chikouche, D. (2014). Adaptive genetic algorithm-based approach to improve the synthesis of two-dimensional finite impulse response filters. IET Signal Processing, 8(5), 429–446.
Dhabal, S., & Venkateswaran, P. (2014). Two-dimensional IIR filter design using simulated annealing based particle swarm optimization. Journal of Optimization 2014, (239721), 10.
Gao, W. F., Liu, S. Y., & Huang, L. L. (2013). A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Transactions on Cybernetics, 43(3), 1011–1024.
Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.
Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697.
Karaboga, D., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 4529, 789–798.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
Kockanat, S., Karaboga, N., & Koza, T. (2012). Image denoising with 2-D FIR filter by using artificial bee colony algorithm. Proceedings International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 1(4), 2–4.
Lai, X. P., & Cheng, Y. (2007). A sequential constrained least-square approach to minimax design of 2-D FIR filters. IEEE Transactions on Circuits and Systems-II, 54(11), 994–998.
Lu, W. S. (2002). A unified approach for the design of 2-D digital filters via semidefinite programming. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 49(6), 814–826.
Lu, W. S., Wang, H. P., & Antoniou, A. (1990). Design of two-dimensional FIR digital filters by using the singular-value decomposition. IEEE Transactions on Circuits and Systems, 37(1), 35–46.
Lu, W. S., Hinamoto, T. (2006). A second-order cone programming approach for minimax design of 2-D FIR filters with low group delay. In Proceedings IEEE international symposium on circuits and systems (pp. 21–24).
McClellan, J. H. (1973). The design of two-dimensional filters by transformations. In Proceedings 7th annual conference on information sciences and systems (pp. 247–251).
Pei, S. C., & Shyu, J. J. (1995). Design of two-dimensional FIR digital filters by McClellan transformation and least-squares contour mapping. Signal Processing, 44(1), 19–26.
Tzeng, S. T. (2007). Design of 2-D FIR digital filters with specified magnitude and group delay responses by GA approach. Signal Processing, 87(9), 2036–2044.
Zhang, X., Zhang, X., Yuen, S. Y., Ho, S. L., & Fu, W. N. (2013). An improved artificial bee colony algorithm for optimal design of electromagnetic devices. IEEE Transactions on Magnetics, 49(8), 4811–4816.
Zhao, R., & Lai, X. (2013). Fast two-dimensional weighted least squares techniques for the design of two-dimensional finite impulse response filters. J Control Theory Applications, 11(2), 180–185.
Zhu, G. P., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217(7), 3166–3173.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dhabal, S., Venkateswaran, P. A novel accelerated artificial bee colony algorithm for optimal design of two dimensional FIR filter. Multidim Syst Sign Process 28, 471–493 (2017). https://doi.org/10.1007/s11045-015-0352-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11045-015-0352-5