Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB- EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD. In this algorithm, the three most diversified individuals are first identified at each iteration, and then half of the best-fitted individuals are forced to be eliminated and repositioned around these diversified agents with equal probability. This process can free the merged best individuals located in a closed populated region and transfer them to the diversified and, thus, less-densely populated regions in the search space. This approach is frequently employed to make the search agents explore the whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shifted classical benchmark functions as well as 29 test problems included in the CEC2017 test suite, and four constrained engineering problems. The results obtained by the proposal upon implemented on the classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newly proposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). The experiments demonstrate the significant superiority of the proposed algorithm when applied to a majority of the test functions, recommending the application of the proposed EPD operator to any other meta-heuristic whenever decided to ameliorate their performance.