By Topic

Performance Evaluation of Evolutionary Algorithms for Optimal Filter Design

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Vural, R.A. ; Dept. of Electron. & Commun. Eng., Yildiz Tech. Univ., Istanbul, Turkey ; Yildirim, T. ; Kadioglu, T. ; Basargan, A.

In analog filter design, component values are selected due to manufactured constant values where performing an exhaustive search on all possible combinations of preferred values for obtaining an optimized design is not feasible. The application of evolutionary algorithms (EA) in analog active filter circuit design and optimization is a promising area which is based on concepts of natural selection and survival of the fittest. In this paper, the performances of genetic algorithm, artificial bee colony optimization, and particle swarm optimization, which are nature-inspired EA techniques, are evaluated for active filter design. Each algorithm is applied to two different filter structures and performances of them are also evaluated when filter design is realized with components selected from different manufactured series.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:16 ,  Issue: 1 )