Recently, many deferential evolution-based algorithms have been developed to solve constrained optimization problems. The performance of these methods outperforms the performance of single operator and/or algorithm-based ones. However, they do not perform consistently for all the problems tested in the literature. Also, the process of using the appropriate selection of algorithms and operators may be time-consuming since their designs are undertaken mainly through trial and error. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple deferential evolution operators, with the best one is emphasized based on the quality and diversity of the population. The performance of the proposed algorithm is tested by solving 57 real-world constrained problems with different dimensions, number of equality and equality constraints, with its results showing a high success rate and that it outperformed different state-of-the-art algorithms.