Self-adapting control parameters with multi-parent crossover in differential evolution algorithm (Q2221583)

Summary: The performance of differential evolution (DE) algorithm is influenced by the setting of control parameters, which is quite dependent on the problem and difficult to be determined. Therefore, the studies on parameter adaptation mechanisms have gradually become more popular. In this paper, we present a self-adaptive DE algorithm (GaDE), in which the adaptation of amplification factor and crossover rate is executed with a multi-parent crossover, while the adaptation timing is decided by the comparative result between the target vector and its offspring. The performance of GaDE algorithm is evaluated on a suite of bound-constrained numerical optimisation problems. The results show that our algorithm is better than, or at least comparable to, the canonical DE, and the two other adaptive DE algorithms.