Aiming at the problems such as weak development ability， low population diversity， and premature convergence to local optimum in Dragonfly Algorithm （DA）， an HDASDE （Hybrid Dragonfly Algorithm based on Subpopulation and Differential Evolution） was proposed. Firstly， the basic dragonfly algorithm was improved： the chaotic factor and purposeful Levy flight were integrated to improve the optimization ability of the dragonfly algorithm， and a chaotic transition mechanism was proposed to enhance the exploration ability of the basic dragonfly algorithm. Secondly， opposition-based learning was introduced on the basis of DE （Differential Evolution） algorithm to strengthen the development ability of DE algorithm. Thirdly， a dynamic double subpopulation strategy was designed to divide the entire population into two dynamically changing subpopulations according to the ability that the subpopulation can improve the algorithm’s ability to jump out of the local optimum. Fourthly， the dynamic subgroup structure was used to fuse the improved dragonfly algorithm and the improved DE algorithm. The fused algorithm had good global exploration ability and strong local development ability. Finally， HDASDE was applied to 13 typical complex function optimization problems and three-bar truss design optimization problem， and was compared with the original DA， DE and other meta-heuristic optimization algorithms. Experimental results show that， HDASDE outperforms DA， DE and ABC （Artificial Bee Colony） algorithms in all 13 test functions， outperforms Particle Swarm Optimization （PSO） algorithm in 12 test functions， and outperforms Grey Wolf Optimizer （GWO） algorithm in 10 test functions. And it performs well in the design optimization problem of three-bar truss.