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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-662-43880-0_31
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