Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

A New Scale Factor for Differential Evolution Optimization

Print
PDF
IJCA Proceedings on National Conference on Communication Technologies & its impact on Next Generation Computing 2012
© 2012 by IJCA Journal
CTNGC - Number 1
Year of Publication: 2012
Authors:
Devendra Tayal
Charu Gupta

Devendra Tayal and Charu Gupta. Article: A New Scale Factor for Differential Evolution Optimization. IJCA Proceedings on National Conference on Communication Technologies & its impact on Next Generation Computing 2012 CTNGC(1):1-5, November 2012. Full text available. BibTeX

@article{key:article,
	author = {Devendra Tayal and Charu Gupta},
	title = {Article: A New Scale Factor for Differential Evolution Optimization},
	journal = {IJCA Proceedings on National Conference on Communication Technologies & its impact on Next Generation Computing 2012},
	year = {2012},
	volume = {CTNGC},
	number = {1},
	pages = {1-5},
	month = {November},
	note = {Full text available}
}

Abstract

In this paper, we propose a new scale factor in differential evolution for optimization of numerical data (low dimensional data) that is both seen in algebraic and exponential form in real world scenarios. With the present work we improve the optimization of DE with real world numerical data set of the Lahi crop production of Pantnagar farm, G. B. Pant University of Agriculture and Technology, Pantnagar, India; inventory demand and population of India. This study focusses on optimization of numerical data that is characterized by single dimension.

References

  • Ardia D. , Boudt K. , Carl P. , Mullen K. M. and Peterson B. G. 2011. Differential Evolution with DEoptim, The R Journal Vol. 3(1).
  • Das, S. , Abraham, A. and Konar, A. , 2008a. Automatic clustering using an improved differential evolution algorithm. IEEE Transactions on Systems, Man and Cybernetics- Part A: Systems and Humans, 38(1).
  • Das S. , Abraham A. and Konar A. , 2008b. Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspective. Studies in Computational Intelligence, Springer, 116, 1-38.
  • Huarng, K. and Yu, H. K. , 2006. Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Transactions on systems, man, and cybernetics—Part B: cybernetics, 36(2), 328–340.
  • Holland, J. H. , 1975. Adaptation in Natural and Artificial Systems. MI: University of Michigan Press, Ann Arbor.
  • Lahmeyer, J. J. , 2003. India, Historical Demographical data of the whole country. [Online] Available at: http://www. populstat. info/ [accessed August 2011]
  • Paterlini, S. and Krink, T. , 2006. Differential evolution and particle swarm optimization. Computational Statistics & Data analysis, 1220-1247.
  • Paterlini S. and Minerva T. , 2003. Evolutionary approaches for cluster analysis. In soft computing applications, A. Bonarini, F. Masulli and G. Pasi, Eds. Berlin, Germany: Springer-Verlag, pp. 167-178.
  • Singh, S. R. , 2008. A computational method of forecasting based on fuzzy time series. Mathematics and Computers in Simulation, 79, 539–554.
  • Storn, R. and Price, K. , 1997. Differential Evolution – A Simple and Ef?cient Heuristic for Global Optimization over Continuous Space. Journal of Global Optimization, 341–359.
  • Qin A. K. , Huang V. L. and Suganthan P. N. , 2009. Differential Evolution Algorithm with Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on evolutionary computation, 13(12).
  • Mininno, E. ; Neri, F. ; Cupertino, F. ; Naso, D. 2011, "Compact Differential Evolution," Evolutionary Computation, IEEE Transactions on, vol. 15, no. 1, pp. 32-54.