Abstract
In this paper we present an empirical , comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Multiple Trial Vectors Differential Evolution algorithms to solve unconstrained global optimization problems. The aim is (1) to compare Multiple Trial Vectors DE, which allows each parent vector in the population to generate more than one trial vector, against the classical DE and (2) to identify the competitive variants which perform reasonably well on problems with different features. The DE and Multiple Trial Vectors DE variants are benchmarked on 6 test functions grouped by features – unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that Multiple Trial Vectors DE compares well with the classical DE.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, Technical Report TR-95-012, ICSI (March 1995)
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic Strategy for Global Optimization and Ccontinuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Price, K.V.: An Introduction to Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. Mc Graw-Hill, UK (1999)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Chakraborty, U.K. (ed.): Advances in Differential Evolution. Studies in Computational Intelligence Series, vol. 143. Springer, Heidelberg (2008)
Montes, E.M., et al.: Multiple Trial Vectors in Differential Evolution for Engineering Design. Engineering Optimization 39(5), 567–589 (2007)
Storn, R.: System Design by Constraint Adaptation and Differential Evolution. IEEE Transactions on Evolutionary Computation 3(1), 22–34 (1999)
Mezura-Montes, E., Velazquez-Reyes, J., Coello Coello, C.A.: A Comparative Study on Differential Evolution Variants for Global Optimization. In: Genetic and Evolutionary Computation Conference, GECCO 2006, July 8-12 (2006)
Babu, B.V., Munawar, S.A.: Optimal Design of Shell-and-Tube Heat Exchanges by Different Strategies of Differential Evolution, Technical Report, PILANI -333 031, Department of Chemical Engineering, Birla Institute of Technology and Science, Rajasthan, India (2001)
Yao, X., Liu, Y., Liang, K.H., Lin, G.: Fast Evolutionary Algorithms. In: Rozenberg, G., Back, T., Eiben, A. (eds.) Advances in Evolutionary Computing: Theory and Applications, pp. 45–94. Springer, New York (2003)
Mezura-Montes, E.: Personal Communication (unpublished)
Feoktistov, V.: Differential Evolution In Search of Solutions. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jeyakumar, G., Velayutham, C.S. (2009). A Comparative Performance Analysis of Multiple Trial Vectors Differential Evolution and Classical Differential Evolution Variants. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-10646-0_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
Online ISBN: 978-3-642-10646-0
eBook Packages: Computer ScienceComputer Science (R0)