Differential evolution; parallel model; hierarchical topology; CEC2013 benchmark suite

A new differential evolution (DE) algorithm with a parallel hierarchical topology (HDE) is proposed. The main goal of the paper is to study how the performance of the algorithm is influenced by the use of parallel migration model. The hierarchical model has several control parameters and the influence of these parameters setting is also studied. The performance of HDE algorithm is compared with non-parallel DE algorithm on CEC2013 benchmark suite. Experimental results show that the HDE outperforms the non-parallel DE algorithm significantly in 27 out of 28 test 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.