Improving the performance of optimization algorithms has long been an important pursuit of researchers. It is a typical design idea and paradigm to combine different optimizers for a synergy of their complementary advantages. Regarding two kinds of population-based evolutionary algorithms, the particle swarm optimizer (PSO) and the differential evolution (DE), we present a systematic and comprehensive survey on their hybrids (DEPSOs) in the literature and propose a taxonomy of hybridization strategies.  Based on the taxonomy, we make a classification of different DEPSOs and analyze their similarities and differences.  We also point out some new directions for future research and provide several guidelines for hybridization design of optimizers.