A New Dual Modifted Bat Algorithm for Design ofAdaptive Noise Canceller

In this paper, the attention focuses on the problem of acoustic noise cancellation using adaptive filtering algorithms based swarm intelligence (SI). In the last decades' nature inspired metaheuristic algorithms such bat algorithm (BA), particle swarm optimization (PSO), grey wolf optimizer (GWO) have been applied to real-world problems such controlling robots, data mining, telecommunication and computer networks, etc. This paper suggests to use a modified Bat algorithm to design a new dual adaptive noise canceller (ANC), we discuss about the limitations of the conventional BA, PSO, and GWO algorithms compared to the proposed one, we present the simulation results that confirm the superiority of Modified BA in term of convergence speed and low steady-state error in comparison with BA, PSO and GWO algorithm behavior which fails when large filters length and non-stationary input are used. The proposed Modified BA algorithm shows significant improvement in the system mismatch (SM) and Output signal-to-noise ratio (SNR) values.