Differential Evolution (DE) is a simple yet efficient stochastic algorithm for solving real world problems. However,
the performance of DE is sensitive to the mutation and crossover strategies and their associated parameters. In this
paper, a kind of scale factor generating scheme within the process of search is proposed, named MSFDE, to enhance
the performance of DE. In this method, the scale factor is a D dimensional matrix which component is a random
number for each difference vector during the iteration. The proposed scheme has been evaluated on a test-suite of 25
benchmark functions provided by CEC 2005 special session on real parameter optimization. The results of the
experiments indicate that MDVDE is competitive to classical DE and some other variants on different strategies.