Evolutionary algorithms have been extensively explored and applied in optimization problems. They allow work with multiple solutions simultaneously, with multimodal functions and dynamic problems, and do not require additional information. Several algorithms have been developed over the years for this task. Yet special attention is needed in the area of increasing the convergence speed of evolutionary algorithms. This study is aimed at developing a framework capable of addressing this new line of research in the field of evolutionary computation. We used the Gaussian Mixture Model to do a local search, and generated a new population through the use of Variational Inference. To implement the proposed framework (GMM- Local Search), NSDE both static and dynamic with multiple objectives were used as basic algorithms. Experiments were performed with different test functions for static and dynamic multi-objective optimization problems. The comparison of the algorithms using the proposed framework with the basic algorithms are presented here, thus evidencing that an improvement in the convergence can be achieved.
