This paper introduces a novel population-based vortex search algorithm (PVS) to improve the weakness of the global search capability of the original Vortex Search (VS) algorithm which is a simple and efficient physics-based metaheuristic and originally has a single-solution-based structure. Single-solution-based metaheuristics perform a local search in the neighborhood of a single solution, whereas population-based metaheuristics conduct the search process by creating several candidate solutions at different points in the search space. The fast-running structures of single-solution-based algorithms may cause the search process to get stuck in the local optimum in some cases. The proposed algorithm transforms VS into a population-based structure with a location update operator and the polynomial mutation operator. Also, the strategy of generating solutions based on the radius reduction mechanism around a center is maintained. Furthermore, two variants of PVS, called PVS_a and PVS_b, are presented in this study. The performance of the proposed approach is investigated by applying a set of experimental series. Three different benchmark sets involving (i) 20 classical benchmark functions, (ii) 29 CEC2017 test functions, and (iii) 10 CEC2019 test functions are employed in experiments. In addition, four real-world-constrained optimization problems are used to evaluate the effectiveness of PVS. Considering the experimental results obtained from the comparison of the proposed algorithm with both state-of-the-art and recent metaheuristics, the developed PVS_a algorithm provides highly quite promising and superior outcomes in solution quality and robustness.