The differential evolution (DE) algorithm, proposed by
Storn and Price [13], is a simple population-based stochastic search technique. DE has been successfully applied in diverse fields such as power systems, mechanical engineering, communication and pattern recognition [14]. In DE, there exist many trials vector generation strategies out of which a few may be suitable for solving a particular problem. Moreover, three crucial control parameters involved in DE, i.e., population size, scaling factor, and crossover rate, may significantly influence the optimization performance of the DE.