Abstract: Many problems in practical project optimization can be attributed to constrained optimization problems (COPs). The main difficulty in dealing with COPs is that there may not be a general algorithm to obtain the global optimal feasible solution when the feasible domain of the problem under consideration is small, discontinuous or multi-peaked. Therefore, it is quite necessary and important to investigate the promising infeasible solutions during the evolution process. Dynamic stochastic selection multi-member differential evolution (DSS-MDE) is effective in this aspect, but it is easily fallen into the local optimal when dealing with some complex constrained optimization problems. In order to deal with this shortcoming, this paper proposes an improved differential evolution algorithm based on dynamic hybrid constrained framework (DHCF-IDE). Firstly, by using the feature information of the current population feasible ratio, the feasible solution search model and the global search model are dynamically implemented. Moreover, the dynamic stochastic ranking and feasibility rule are taken as the constraint processing method for the two models, respectively. Secondly, the multimember differential evolution and the proposed improved differential evolution based on power-law distribution of parental selection are used for the two models, respectively. Finally, the simulation experiments are made via six complex benchmark functions chosen from CEC2006. Compared with DSS-MDE or dynamic hybrid framework (DyHF), the proposed DHCF-IDE can keep better convergence rate and global search ability. Furthermore, the feasibility of the improved algorithm in practical application is also demonstrated by the industrial case of maximizing aromatics yield in catalytic reforming.