Title: Bacteria foraging optimisation algorithm for optimal FIR filter design

Authors: Suman Kumar Saha; Rajib Kar; Durbadal Mandal; Sakti Prasad Ghoshal

Addresses: Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Electronics and Communication Engineering Department, National Institute of Technology Durgapur, West Bengal, 713209, India ' Department of Electrical Engineering, National Institute of Technology Durgapur, West Bengal, 713209, India

Abstract: This paper presents a novel search algorithm, called bacteria foraging optimisation (BFO) for the design of linear phase positive symmetric FIR low pass, high pass, band pass and band stop filters, realising the respective ideal filter specifications. BFO is a population-based evolutionary optimisation concept used to solve nonlinear optimisation problem where each individual maintains the propagation of genes. BFO favours propagation of genes of those animals which have efficient foraging strategies and eliminate those animals that have weak foraging strategies i.e., method of finding, handling and taking in food. All animals with their own physiological and environmental constraints, try to maximise the consumption of energy per unit time interval. The performances of BFO-based FIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA) and standard particle swarm optimisation (PSO) optimisation techniques. The simulation results justify that BFO is the best optimiser among the other optimisation techniques, not only in the convergence speed but also in the accuracy and the optimal performances of the designed filters.

Keywords: FIR filters; filter design; real coded genetic algorithms; RGA; particle swarm optimisation; PSO; bacteria foraging optimisation; BFO; evolutionary optimisation; magnitude response; convergence; optimal design.

DOI: 10.1504/IJBIC.2013.053039

International Journal of Bio-Inspired Computation, 2013 Vol.5 No.1, pp.52 - 66

Received: 31 Jan 2012
Accepted: 13 Aug 2012
Published online: 31 Mar 2014 *

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