Differential evolution with preferential crossover
We study the mutation operation of the differential evolution algorithm. In particular, we study the effect of the scaling parameter of the differential vector in mutation. We derive the probability density function of points generated by mutation and thereby identify some drawbacks of the scaling parameter. We also visualize the drawbacks using simulation. We then propose a crossover rule, called the preferential crossover rule, to reduce the drawbacks. The preferential crossover rule uses points from an auxiliary population set. We also introduce a variable scaling parameter in mutation. Motivations for these changes are provided. A numerical study is carried out using 50 test problems, many of which are inspired by practical applications. Numerical results suggest that the proposed modification reduces the number of function evaluations and cpu time considerably.
differential evolution crossover mutation probability density function auxiliary population set global optimization continuous variable