Comparative Analysis of Meta-Heuristic-Driven Fractional Derivative Constraints for Linear-Phase HP and BP FIR Filter Design with Hybrid L1- L2 Norm

In this manuscript, an optimal linear phase High pass (HP) and Band pass (BP) FIR filter is designed and analysed through various performance parameters. The proposed method employs a hybrid approach that integrates fractional derivatives (FD) constraints with natureinspired optimization algorithm EBMO (Enhanced barnacle mating optimization) to resolve the design problem. The optimal values of the fractional derivative constraints are obtained by minimizing a hybrid L 1 and L 2 Norm cost function applying EBMO algorithm. The cost function is a hybridization of L1 & L 2 norms of both the passband and stopband regions of HP and BP FIR filters respectively. Additionally, HP and BP FIR filters are also designed using hybrid cost function and TSO (Tyrannosaurus optimization algorithm) SFLA (Shuffled Frog Leaping Algorithm), WO (Walrus optimization), and GGO (Greylag goose optimization) algorithms optimized fractional derivative constraints. Comparative analysis with existing methods highlights the superior performance and efficiency of the proposed algorithm.