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 ( \(dmvD^{2}E)\) . 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 \(dmvD^{2}E\) . The \(dmvD^{2}E\) , 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 \(dmvD^{2}E\) as whole. The \(dmvD^{2}E\) 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 \(dmvD^{2}E\) , 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.