Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm

The research on optimal design of Infinite-impulse response (IIR) filter design based on various optimization techniques including evolutionary algorithms EA’s, has gained much attention in recent years. Previously, digital IIR filter’s parameters are encoded using floating point representation. Fixed point representation are used for encoding of digital IIR filter’s parameters because it effectively save computational resources and more convenient for direct realization on hardware. On comparing fixed point representation with floating point representation, fixed point representation would make the search space miss much useful gradient information and therefore raises new challenges for continuous EA’s. In this paper, first we will design digital low pass IIR filter. Then, analyze the fitness landscape properties of optimal digital low pass IIR filter. Based upon the fitness landscape investigation, apply two-state ensemble evolutionary algorithm to the above design optimal digital low pass IIR filter with fixed-point representation. In order to evaluate the performance of TEEA, we experimentally compare it with SaDE, jaDE, CLPSO, MUEDA on Low-pass, High-pass, Band-pass, Band-stop digital IIR filters with 10 different settings. Comparison is based on the standard deviation, mean and the number of successful runs. Based on the experimentally compared result, we conclude that TEEA has higher convergence speed, better exploration, and higher success rate. In order to benchmark TEEA, we apply TEEA further to some more difficult problems with shorter word length and higher order. TEEA perform satisfactory on task of shorter word length and higher order as