The optimization of swarm intelligence algorithms is a main way to improve swarm intelligence algorithms. As the swarm intelligence algorithms are more and more widely used in all kinds of model optimization， production scheduling， path planning and other problems， the demand for performance of intelligent algorithms is also getting higher and higher. As an important means to optimize swarm intelligence algorithms， subgroup strategies can balance the global exploration ability and local exploitation ability flexibly， and has become one of the research hotspots of swarm intelligence algorithms. In order to promote the development and application of subgroup strategies， the dynamic subgroup strategy， the subgroup strategy based on master-slave paradigm， and the subgroup strategy based on network structure were investigated in detail. The structural characteristics， improvement methods and application scenarios of various subgroup strategies were expounded. Finally， the current problems and the future research trends and development directions of the subgroup strategies were summarized.

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.