Many real-world multi-objective optimization problems are dynamic. These problems require an optimization algorithm to quickly track optimal solutions after changing the environment. In most dynamic multi-objective optimization algorithms, response mechanisms are used to generate the initial population after the environment changes. In the present study, a novel Combinational Response Mechanism (CRM) is proposed, which consists of three parts. After detecting the environmental change, in the first part, RM-rand, a subpopulation of random solutions is generated using DE/rand/1 operator and Cauchy mutation. The second part, RM-Tr&SP, predicts a subpopulation of solutions using transfer learning (TL) and special points. The third part, RM-M, uses the best solutions of the previous environment with a propagation method based on crowding distance to generate the third subpopulation. A combination of the solutions of these three subpopulations is considered the initial population of the new environment. The proposed response mechanism can converge the set of solutions while maintaining their diversity. Thus, generating solutions with good convergence and diversity makes the initial population more adaptable to the new environment. The examinations were done on 24 common test functions. The experimental results indicate that the performance of the proposed response mechanism in dynamic multi-objective optimization is competitive with five advanced evolutionary algorithms.