A parallel tabu search algorithm for digital filter design

Abstract

Purpose -The purpose of the paper is to present a novel design method for the optimal finite word length (FWL) finite impulse response (FIR) filters. Design/methodology/approach -The design method is based on a parallel tabu search (TS) algorithm which uses the crossover operator of the genetic algorithm. Findings -Three design examples have been presented to show that the proposed method can provide a good solution to the design problem of a FWL FIR filter. In order to show the validity of the proposed method, the performance of the suggested method has been compared to those of widely-used other methods. From the comparison results, it was concluded that the proposed method can be efficiently used for the optimal FWL FIR filter design.Research limitations/implications -The number of examples can be increased and also the performance of the proposed method might be compared to other design methods, apart from those presented in this work, developed for the design of optimal FWL FIR filters. Practical implications -The use of this method produces optimal digital FWL FIR filters with low complexity and therefore provides advantages in the terms of speed and cost. Originality/value -The originality is the application of the parallel TS algorithm described by the authors to the FWL FIR filter design. The work presented in the paper is particularly important for the researchers studying on the design methods for FWL FIR filter design and the applications of these type filters.

Fetal Electrocardiogram Extraction using Moth Flame Optimization (MFO)-Based Adaptive Filter

“…Gradient-based optimization algorithms are popular, but they have the problem of convergence at local minimum for multimodal error surfaces [10]. Attempts to solve the problem of local minima and achieve global optimum solution has led many researchers to introduce the use of global optimization techniques for adaptive filter optimization such as Genetic Algorithm (GA) [11], Simulated Annealing (SA) [12], Tabu Search (TS) [13], Differential Evolution (DE) [14], Particle Swarm Optimization (PSO) [15], Ant-colony (ACO) [16], Artificial intelligence [17] Modified firefly and modified ABC algorithms [18].…”