Review And Analysis Of Optimization Algorithms For Digital Filter Design

Abstract

In general Digital Signal Processing (DSP) and more especially filtering is an important and basic requirement for signal systems, computers, and communication networks. The design of Optimal Digital Filters is a intimidating task and it has challenged the scientist, engineers, and researchers for designing the filters with improvised, proficient, and intelligent techniques using the Emerging Evolutionary Computations. Metaheuristics have emerged as the best promising tool and an striking area of research with numerous improvements and advancements in the solution to optimization issues. However, it has not shown clarity to decide the best performing metaheuristic for designing an optimal digital filter. In this paper, a comprehensive review and analysis of various metaheuristics used by researchers for designing an optimal digital filter are carried out. More specifically, “Finite Impulse Response (FIR)” and “Infinite Impulse Response (IIR)” filter design using optimization-based techniques such as nature-inspired “Swarm Intelligence (SI) Swarm Intelligence (SI), Cuckoo Search (CS), Grasshopper Optimization Algorithms,, Particle Swarm Optimization, (PSO), Ant Colony Optimization (ACO Bat Algorithms (BA), Genetic Algorithms (GA), Artificial Bee Colony (ABC), Bacterial foraging optimization (BFO, Biogeography-based optimization (BBO), Harmony search (HS), Krill herd (KH), Social spider optimization (SSO), Symbiotic organisms search (SOS), Firefly algorithm (FA), Gravitational search algorithm (GSA), Grey wolf algorithm (GWO), Teaching-learning-based optimization (TLBO), Whale optimization algorithm (WOA)” are also described. Also, a survey on the origin of twenty-one optimization algorithms is carried out that is being proposed as optimization algorithms in literature for the proposal of digital filters.