Improved Differential Evolutions Using a Dynamic Differential Factor and Population Diversity

As a new kind of evolutionary algorithms, differential evolution (DE) has attracted much attention in solving optimization problems in the last few years. To accelerate its convergence rate and enhance its performances, this paper introduces a dynamic adjustment method for the differential factor and a modified version of mutation strategy into DE. Furthermore, a disturbance approach based on population diversity is used to further improve the search capability. Thus, two improved DE, IDE1 and IDE2, are presented. The performances of the IDE1 and IDE2 are evaluated on seven complex benchmark functions with three different dimensionalities. Experimental results show that the performances of IDE1 and IDE2 are superior to other two DEs in terms of convergence rates and qualities of solutions.