CFP last date
20 August 2024
Reseach Article

Role of Optimization Techniques in Engineering

by Shikha Tripathi, Bharti
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 7
Year of Publication: 2016
Authors: Shikha Tripathi, Bharti

Shikha Tripathi, Bharti . Role of Optimization Techniques in Engineering. International Journal of Computer Applications. 142, 7 ( May 2016), 1-6. DOI=10.5120/ijca2016909846

@article{ 10.5120/ijca2016909846,
author = { Shikha Tripathi, Bharti },
title = { Role of Optimization Techniques in Engineering },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 7 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016909846 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:44:17.868323+05:30
%A Shikha Tripathi
%A Bharti
%T Role of Optimization Techniques in Engineering
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 7
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

By virtue of Optimization one can minimize or maximize a particular function in a finite dimensional Euclidean space over a subset of that space, which is generally determined by functional inequalities. It is the result of continuous research that Optimization has been evolve into an established field and had expanded in many branches like linear conic optimization, convex optimization, global optimization, discrete optimization, etc. Each of such branches has a sound theoretical foundation and is featured by an extensive collection of sophisticated algorithms and software. Optimization, as a powerful modeling and problem solving methodology, has a broad range of applications in management science, industry and engineering. The main concern of optimized design is the finding of optimum parameters according to a given optimality standard. To cope up with the current development in engineering and other fields we must have to update over optimization techniques which can be use for the non-differentiable, not continuous objective functions. Every optimization techniques have its own merits and demerits and may be good for any particular purpose and may be worst for some other purpose. Like application of global optimization algorithm is sometimes a very time consuming task. The best local optimization methods for this purpose are the gradient methods. So in this work an intelligent way of using these optimization techniques is being presented which illustrate the fact that which techniques or a combination of techniques may be efficiently used for a given purpose. For that we have demonstrated the use of global optimization in two different tasks one is optimization of step size of LMS algorithm using Ant Colony Optimization (ACO) & Particle swarm optimization (PSO) and the other is designing of an Analog Sallen Key Band Pass filter using ACO. Simulation of each case using MATLAB is done to prove the validity of optimized result and optimized designing.

  1. Paulo S. R. Diniz, Adaptive Filtering Algorithms and Practical Implementations, Springer, USA, 2008.
  2. S. Haykin, Adaptive Filter Theory, Prentice Hall, USA, 2002.
  3. D. J. Krusienski, W. K. Jenkins, Design and performance of adaptive systems based on structured stochastic optimization strategies, IEEE Circuits Systems Magazine 5 (2005), pp. 8-20.
  4. S. C. Ng, S. H. Leung, C. Y. Chung, A. Luk, W. H. Lau, The genetic search approach: A new learning algorithm for adaptive IIR filtering, IEEE Signal Processing Magazine 13 (1996), pp. 38-46.
  5. N. Karaboga, Digital IIR filter design using differential evolution algorithm, EURASIP Journal on Applied Signal Processing 8 (2005), pp. 1-9.
  6. A. Kalinli, N. Karaboga, A parallel tabu search algorithm for digital filter design, COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 24 (2005), pp. 1284-1298.
  7. N. Karaboga, B. Cetinkaya, Design of digital FIR filters using differential evolution algorithm, Circuits Systems and Signal Processing Journal 25 (2006) , pp. 649-660.
  8. Variable-Step-Size-Normalized-LMS-Adaptive-Algorithm-for-Echo-Cancellation. LMS Algorithm.
  9. P. Visu and E. Kannan, Traffic Parameterized ACO for Ad-Hoc Routing Hansen E.R.: Global optimization using interval methods. New York, Marcel Dekker,1992.
  10. A. Kalınlı, N. Karaboga, A new method for adaptive IIR filter design based on tabu search algorithm, International Journal of Electronics and Communication 59 (2004), pp. 1-7.
  11. S. Chen, B. L. Luk, Adaptive simulated annealing for optimization in signal processing applications, Signal Processing 79 (1999), pp. 117-128.
  12. N. Karaboga, A new design method based on artificial bee colony algorithm for digital IIR filters, Journal of the Franklin Institute-Engineering and Applied Mathematics 346 (2009), pp. 328-348.
  13. N. KARABOGA, A. KALINI, D. KARABOGA, Designing digital IIR filter using ant colony optimization algorithm. Engineering applications of artificial intelligence april 2004. Vol.17 (3)
  14. Dissanayake, S.D. Performance analysis of noise cancellation in a diversity combined ACO-OFDM system. ICTON, 2012
  15. P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical Implementations, Kluwer Academic Publishers, Boston, 1997.
  16. P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical Implementations, Kluwer Academic Publishers, Boston, 1997.
  17. IoanTabus, Stochastic gradient based adaptation: Least Mean Square (LMS)Algorithm, SGN 21006 Advanced Signal Processing:Lecture 5
  18. P. Visu and E. Kannan, Traffic Parameterized ACO for Ad-Hoc Routing
  19. Ali M. and Babak, A. “A new clustering algorithm based on hybrid global optimization based on a dynamical systems approach algorithm”, Expert Systems with Applications (Elsevier), Vol. 37,pp. 5645-5652, 2010
  20. Dorigo, M. and Stutzle, T. “Ant Colony Optimization”, MIT Press, Cambrige MA, 2004
  21. Dorigo, M., Maniezzo, V. and Colorni, A., “Ant System: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics—Part B, Vol. 26, No. 1, pp. 29 – 41, 1996
  22. Frank, N. and Carsten, W. “Ant Colony Optimization and the minimum spanning tree problem”, Theoretical Computer Science (Elsevier), Vol. 411, pp. 2406-2413, 2010
  23. Goss, Aron, Deneubourg, and Pasteels, “Selforganized shortcuts in the Argentine ant,” Naturwissenschaften, Vol. 76, pp. 579–581, 1989
  24. Hsin-Yun, L., Hao-Hsi, T., Meng-Cong, Z. and Pei-Ying, L. “Decision support for the maintenance management of green areas”, Expert Systems with Applications (Elsevier), Vol. 37, pp. 4479- 4487, 2010
Index Terms

Computer Science
Information Sciences


Optimization Global Optimization Techniques Ant Colony Optimization Particle Swan Optimization Sallen key band pass filter.