Efficient Differential Evolution Based on Evolutional Information Matrix

Li-Li Zhu, Hua Xu, Ai-Fen Wang

Abstract: Differential evolution (DE) is a population-based algorithm which has been widely-used in various science and engineering fields. Much recent attention has aimed to improve the DE global convergence by altering the control parameters CR and F. However, the structure and implementation of DEs has become increasingly more complicated. This paper investigated a more efficient and simple differential evolution algorithm based on an evolutional information matrix (EMDE). An evolutional information matrix which includes crossover and mutation amplitude information is proposed. Additionally, the EMDE algorithm uses a memory population generated by a previous population to control the population search direction. Experimental results based on 12 standard benchmarks indicate that the EMDE algorithm outperforms other comparable DE variants.