Seeker Optimization Algorithm for Digital IIR Filter Design

Since the error surface of digital infinite-impulse-response (IIR) filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a seeker-optimization-algorithm (SOA)-based evolutionary method is proposed for digital IIR filter design. SOA is based on the concept of simulating the act of human searching in which the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The algorithm's performance is studied with comparison of three versions of differential evolution algorithms, four versions of particle swarm optimization algorithms, and genetic algorithm. The simulation results show that SOA is superior or comparable to the other algorithms for the employed examples and can be efficiently used for IIR filter design.

I. Introduction

With the advantages of digital signal processing (DSP) over an analog system [1], [2], digital filters have attracted increasing attention in recent years due to demands for improved performance in high-data-rate digital communication systems and in wideband image/video processing systems. Digital infinite-impulse-response (IIR) filters can often provide a much better performance and less computational cost than their equivalent finite-impulse-response (FIR) filters [3] and have become the target of growing interest [1], [2], [4]. However, because the error surface of IIR filters is usually nonlinear and multimodal, conventional gradient-based design methods may easily get stuck in the local minima of error surface [4], [5]. Therefore, some researchers have attempted to develop design methods based on modern heuristic optimization algorithms such as genetic algorithm (GA) [6]–[9], simulated annealing (SA) [10], tabu search (TS) [5], ant colony optimization (ACO) [1], immune algorithm (IA) [2], differential evolution (DE) [11], [12], and particle swarm optimization (PSO) [13], [14], etc.