Authors:
Alcemy G. V. Severino
;
Leandro L. S. Linhares
and
Fábio M. U. de Araújo
Affiliation:
Federal University of Rio Grande do Norte, Brazil
Keyword(s):
FIR Filter Design, Bat Algorithm, Particle Swarm, Optimization.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Evolutionary Computation and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
In this paper, the traditional metaheuristic Particle Swarm Optmization (PSO) and the Bat Algorithm (BA)
are used to optimal design digital low pass (LP) Finite Impulse Response (FIR) filters. These filters have a
wide range of applications because of their characteristics. They are easy to be designed, they have guaranteed
bounded input-bounded output (BIBO) stability and can be designed to present linear phase at all frequencies.
Traditional optimization methods based on gradient are susceptible to getting trapped on a local optima solution
when they are applied to optimize multimodal problems, such as the FIR filter design. Here, to overcome
this drawback, the aforementioned metaheuristics are adopted to obtain the coefficients of low pass FIR filters
of order 20 and 24. The performance of BA and PSO algorithms are compared with the classical Parks and
McClellan (PM) filter design algorithm, which is a deterministic procedure. For this comparison is considered
the filters pass ban
d and stop band ripples, transition width and statistical data. The simulation results
demonstrate that the proposed filter design approach using BA algorithm outperforms PM and PSO.
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