Abstract
This paper presents an empirical study of a micro Differential Evolution algorithm (micro-DE) performance versus a canonical Differential Evolution (DE) algorithm performance. Micro-DE is a DE algorithm with reduced population and some other differences. This paper’s objective is to show that our micro-DE outperforms the canonical DE for large scale optimization problems by using a test bed consisting of 20 complex functions with high dimensionality for a performance comparison between the algorithms. The results show two important points; first, the relevance of an accurate set of the optimization algorithms parameters regarding the problem itself. Second, we demonstrate the superior performance of our micro-DE with respect to DE in 19 out 20 tested functions. In some functions, the difference is up to seven orders of magnitude. Also, we show that micro-DE is better statistically than a simple DE and an adjusted DE for high dimensionality. In several problems where DE is used, micro-DE is highly recommended, as it achieves better results and statistic behavior without much change in code.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Goldberg, D.E.: Genetic algorithms, noise, and the sizing of populations. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, California, pp. 70–79. Morgan Kaufmann Pub (1989)
Herrera, F., Lozano, M., Molina, D.: Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other meta-heuristics for large scale continuous optimization problems. Technical report, SCI2S, University of Granada, Spain (2010)
Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. In: SPIE Proceedings: Intelligent Control and Adaptive Systems, Philadelphia, PA, vol. 1196, pp. 289–296 (1989)
Parsopoulos, K.E.: Cooperative micro-differential evolution for high-dimensional problems. In: Proceedings of the GECCO’09, Montréal Québec, Canada, pp. 531–538. ACM (2009). ISBN 978-1-60558-325-9/09/07
Rahnamayan, S., Tizhoosh, H.R.: Image thresholding using micro opposition-based differential evolution (Micro-ODE). In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2008), pp. 1409–1416 (2008)
Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, TR-95-012, ICSI (1995). ftp://ftp.icsi.berkeley.edu
Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the CEC’2010 special session and competition on large scale global optimization. Technical report, NICAL, USTC, Hefei, Anhui, China (2009)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 1, 86–92 (1940)
Acknowledgments
Authors would like to thank, for their economic support:
– “Instituto Politécnico Nacional”, CONACyT (register number 175589 and 290674), SNI, COFAA (register number SeAca/COTEPABE/79/12), Academic Secretary, Postgraduate and Research Secretary.
– The project roadMe: Fundamentals for Real World Applications of Metaheuristics: The Vehicular Network Case TIN2011-28194 (2012-2014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olguin-Carbajal, M., Herrera-Lozada, J.C., Arellano-Verdejo, J., Barron-Fernandez, R., Taud, H. (2014). Micro Differential Evolution Performance Empirical Study for High Dimensional Optimization Problems. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-662-43880-0_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43879-4
Online ISBN: 978-3-662-43880-0
eBook Packages: Computer ScienceComputer Science (R0)