Design of Modified Bacterial Foraging Algorithm (MBFA) FIR Filter for Signal Processing Applications

Finite Impulse Response (FIR) filters are widely used in digital signal processing (DSP) software, and optimization is required for better performance, faster computation, and flexibility. This paper describes the Modified Bacterial Foraging Algorithm (MBFA) to optimize the FIR filter coefficients that addresses major concerns in power usage, resource utilization, and rate of processing. The new design integrates Common Subexpression Elimination (CSE) and Canonical Signed Digit (CSD) methods for the elimination of redundant computation, decreasing hardware complexity, and improving energy efficiency. The architecture minimizes redundant multiplications and leverages best coefficient values to reduce power consumption and area significantly without sacrificing accuracy. The FIR filter is designed in Xilinx Vivado, and performance is measured in terms of important parameters like power consumption, delay, and resource utilization. Relative comparison with traditional optimization methods confirms the dominance of the MBFA-based FIR filter in the achievement of an optimal trade-off between performance and computational complexity. The results confirm MBFA as a strong and powerful optimization framework for improving the overall efficiency of FIR filter design. This study contributes to the design of low-power and high-speed DSP systems and is thus relevant in high-performance applications that require real-time processing