Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization

• Published in: International journal of electronics and communications Vol. 114; p. 153019
• Main Authors: Dash, Judhisthir, Dam, Bivas, Swain, Rajkishore
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.02.2020
• Subjects: FIR filter; Hybrid differential evolution particle swarm optimization (HDEPSO); Normalized error fitness; Sharp band edge frequency; FIR filter; Hybrid differential evolution particle swarm optimization (HDEPSO); Normalized error fitness; Sharp band edge frequency
• ISSN: 1434-8411; 1618-0399
• DOI: 10.1016/j.aeue.2019.153019

•Optimal design/implementation of sharp edge FIR filter (SEFIRF) is proposed.•Design of SEFIRF is focused as a multi-objective and multimodal optimization problem.•To guarantee the main objectives of a SEFIRF, a novel NEF function is developed.•Robust HDEPSO algorithm is proposed for the effectiveness of the SEFIRF. The filter is an important building block of modern communication and electronic systems. Based on well-defined bandwidth, it extracts the desired portion of the spectrum when the raw input signal is applied. Efficacy of filtering depends on the preciseness of bandwidth to avoid co-channel interference and signal loss. In addition, it must provide higher stopband attenuation and passband attenuation very close to unity with a tolerable quantity of pass/stop band ripple. Design/implementation of sharp edge modern FIR filter is structured as a multi-objective, constrained, complex, and highly nonlinear (hence multimodal) error minimization challenge. Hence, this work proposes a novel objective (normalized error fitness) function and a robust hybrid algorithm for the effective searching of the optimal filter coefficients for providing excellent sharp edge frequency response during the filtering action. Most popular particle swarm optimization (PSO) and differential evolution (DE) algorithm are effectively combined together to frame the proposed hybrid DE-PSO algorithm for enhancing the exploration and exploitation abilities of it. The proposed hybrid algorithm is validated using twelve different benchmark functions. Through simulations, the qualitative performance of the proposed approach is compared with the conventional PSO, DE, real-coded genetic algorithm and the Parks–McClellan method.