Active Noise Control using Bacterial Foraging Optimization Algorithm

This paper proposes a nonlinear ANC system in which a new evolutionary algorithm based on the foraging behavior of E. coli bacteria is used for adaptive nonlinear filter coefficients optimization, called BFA_ANC. While the standard LMS based nonlinear filters may converge to local minima, the evolutionary algorithms may handle this problem efficiently and converge to the global minima. In addition, this class of algorithms does not require the identification of the secondary paths. Computer simulations show the major improvements in final residual noise, as well as the processing time of the proposed ANC system in comparison to the other techniques in the literature.