Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization

This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution ($$dmvD^{2}E)$$dmvD2E). This novel framework is a heterogeneous mix of effective differential evolution (DE) and dynamic differential evolution (DDE) variants with diverse characteristics in a distributed framework to result in $$dmvD^{2}E$$dmvD2E. The $$dmvD^{2}E$$dmvD2E, discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of $$dmvD^{2}E$$dmvD2E as whole. The $$dmvD^{2}E$$dmvD2E variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The $$dmvD^{2}E$$dmvD2E, when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.