Micro Differential Evolution Performance Empirical Study for High Dimensional Optimization Problems

Abstract

AbstractThis 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.KeywordsMicro-algorithmReduced populationDifferential evolutionHigh dimensionality