A Metaheuristic Approach to Two Dimensional Recursive Digital Filter Design

The two dimensional IIR digital filter design problem has received increased attention over the past few years. Recently, several metaheuristic algorithms have been employed in this domain and have produced promising results. Invasive Weed Optimization is one of the latest population-based metaheuristic algorithms that mimics the colonizing action of weeds. In this chapter, an improvement to the classical weed optimization algorithm has been proposed by introducing a constriction factor in the seed dispersal phase. Temporal Difference Q-Learning has been employed to adapt this parameter for different population members through the successive generations. Such hybridization falls under a special class of adaptive Memetic Algorithms. The proposed memetic realization, called Intelligent Invasive Weed Optimization (IIWO), has been applied to the two-dimensional recursive digital filter design problem and it has outperformed several competitive algorithms that have been applied in this research field in the past.

[1]  Nikos E. Mastorakis,et al.  Design of two-dimensional recursive filters by using neural networks , 2001, IEEE Trans. Neural Networks.

[2]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[3]  G. Maria,et al.  An l p design technique for two-dimensional digital recursive filters , 1974 .

[4]  Amit Konar,et al.  A swarm intelligence approach to the synthesis of two-dimensional IIR filters , 2007, Eng. Appl. Artif. Intell..

[5]  Amit Konar,et al.  A Multi-Objective Memetic Optimization Approach to the Circular Antenna Array Design Problem , 2012 .

[6]  M. Swamy,et al.  Quadrantal symmetry associated with two-dimensional digital transfer functions , 1978 .

[7]  Kay Chen Tan,et al.  A Multi-Facet Survey on Memetic Computation , 2011, IEEE Transactions on Evolutionary Computation.

[8]  Weiping Zhu,et al.  A closed-form solution to the least-square design problem of 2-D linear-phase FIR filters , 1997 .

[9]  K. M. Sim,et al.  Guest Editorial Special Issue on Game-Theoretic Analysis and Stochastic Simulation of Negotiation Agents , 2006 .

[10]  S. Ovaska,et al.  Design and implementation of efficient IIR notch filters with quantization error feedback , 1994 .

[11]  T. Kaczorek.  Two-Dimensional Linear Systems , 1985 .

[12]  Pratyusha Rakshit,et al.  DE-TDQL: An adaptive memetic algorithm , 2012, 2012 IEEE Congress on Evolutionary Computation.

[13]  Spyros G. Tzafestas,et al.  Multidimensional Systems: Techniques and Applications , 1986 .

[14]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[15]  Nikos E. Mastorakis,et al.  Evolutionary design of 2-dimensional recursive filters via the computer language GENETICA , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[16]  Andreas Antoniou,et al.  Two-Dimensional Digital Filters , 2020 .

[17]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[18]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Chris Watkins,et al.  Learning from delayed rewards , 1989 .

[20]  A. Willsky,et al.  Efficient implementations of 2-D noncausal IIR filters , 1997 .

[21]  Amit Konar,et al.  An Adaptive Memetic Algorithm using a synergy of Differential Evolution and Learning Automata , 2012, 2012 IEEE Congress on Evolutionary Computation.

[22]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[23]  Bogdan Dumitrescu,et al.  Optimization of two-dimensional IIR filters with nonseparable and separable denominator , 2005, IEEE Transactions on Signal Processing.

[24]  M.N.S. Swamy,et al.  Design of two-dimensional recursive filters using genetic algorithms , 2003 .

[25]  C. Kuo,et al.  Design of two-dimensional FIR digital filters by a two-dimensional WLS technique , 1997 .

[26]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .