Evolutionary algorithms (EAs) are known to be one of the most successful Nature-inspired techniques to deal with complex optimization problems. Indeed, the key feature of EAs is their ability to achieve a proper ratio between exploration and exploitation. However, when dealing with dynamic optimization problems (DOPs), EAs are often challenged by convergence which may brake the adaptation after changes. In such cases, repairing actions need to be taken to enable the algorithm to perform better. Our proposal here is to monitor the behavior of EAs based on a diversity test to determine if the current population is efficiently prepared to give rise to better individuals. Hence, an adjustment scheme is used before resuming the regular working steps of the algorithm. To validate the proposed approach, we conduct a series of experiments and compare it gainst several approaches from the state of the art, and in terms of several performance measures. It is shown that the proposed scheme improves the performance of EAs and outperforms competing algorithms.

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