The design of two-dimensional Infinite Impulse Response (2D-IIR) filters has recently attracted attention in several areas of engineering because of their wide range of applications. Synthesizing a user-defined filter in a 2D-IIR structure can be interpreted as an optimization problem. However, since 2D-IIR filters can easily produce unstable transfer functions, they tend to compose multimodal error surfaces, which are computationally difficult to optimize. On the other hand, Evolutionary Computation (EC) algorithms are well-known global optimization methods with the capacity to explore complex search spaces for a suitable solution. Every EC technique holds distinctive attributes to properly satisfy particular requirements of specific problems. Hence, a particular EC algorithm is not able to solve all problems adequately. To determine the advantages and flaws of EC techniques, their correct evaluation is a critical task in the computational intelligence community. Furthermore, EC algorithms are stochastic processes with random operations. Under such conditions, for obtaining significant conclusions, appropriate statistical methods must be considered. Although several comparisons among EC methods have been reported in the literature, their conclusions are based on a set of synthetic functions, without considering the context of the problem or appropriate statistical treatment. This paper presents a comparative study of various EC techniques currently in use employed for designing 2D-IIR digital filters. The results of several experiments are presented and statistically analyzed.
A Dual-Band Microwave Filter Design for Modern Wireless Communication Systems
