global optimization, differential evolution, self-adaptation, parallel model, experimental comparison

Six adaptive variants of differential evolution are applied in a parallel migration model with a star topology. The parallel algorithm with several different settings of parameters controlling the migration was experimentally compared with the adaptive serial algorithms in benchmark problems of dimension $D=30$. The parallel algorithm was more efficient than the best serial adaptive DE variant in a half of the problems.

The problem of optimal partitioning by minimizing pooled-within-variance of groups is addressed. Three state-of-the-art adaptive differential evolution algorithms are compared on four real-world data sets. A~novel hybrid differential evolution algorithm, including k-means algorithm for local search is proposed. The experimental comparison is done with either the plain adaptive differential evolution variants or the hybrid algorithms. Experimental results showed that hybrid algorithms are substantially better preforming when compared with plain differential evolution variants. Among hybrid variants, the competitive differential evolution appeared to be the most efficient.