Non-linear active noise cancellation using a bacterial foraging optimisation algorithm

This study presents a new scheme for non-linear active noise control (ANC) systems. In the proposed ANC system, a new evolutionary algorithm known as bacterial foraging (BF) is used for optimising the adaptive controller. The proposed ANC system using bacterial foraging optimisation (BFO) has the ability to prevent falling into local minima. Moreover, using the BF algorithm to adapt the ANC filter coefficients removes the need for the preliminary identification of the secondary path. Several computer simulations are developed in order to analyse the performance of the proposed BFO-based ANC system (BFO-ANC). The experiments are carried out in two major groups including a linear and a non-linear secondary path, along with a non-linear primary path. In each group, the effect of different parameters of the BFO algorithm is investigated on the performance and robustness of the proposed ANC system. The authors also compare the results obtained by three ANC systems; the proposed BFO-based ANC, the GA-based ANC and the filtered-X LMS-based ANC. Simulation results demonstrate the effectiveness of the proposed BFO method in noise cancellation performance under several situations.