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.