Improved bee colony algorithm based on knowledge strategy for digital filter design

Abstract

An improved bee colony algorithm (IBCA) is proposed, which is a global stochastic searching optimisation algorithm possessing properties of both cultural algorithm and artificial bee colony (ABC) algorithm. Digital filter design involves multi-parameter optimisation, on which the existing optimisation algorithms don't work efficiently. This paper focuses on introducing IBCA and its performance in designing FIR digital filter and IIR digital filter. After presenting the basic knowledge about IBCA, we show how to implement it in FIR digital filter and IIR digital filter design with some adaptive measures to enhance its performance. It has been demonstrated by simulation results that the proposed IBCA outperforms particle swarm optimisation (PSO), quantum particle swarm optimisation (QPSO) and ABC algorithms in finding out the global optima of the problem more rapidly.