The predator search marine predators algorithm PSMPA is a new algorithm that incorporates hybrid search operators and competitive learning to address the problems of population diversity loss, low solution precision, and difficulty escaping local optima in the later iterations of the classic marine predator algorithm (MPA). The introduction of a stochastic dynamic centroid opposition-based learning mechanism enhances population diversity in the later stages of the algorithm, expands the search space, and improves the algorithm’s ability to escape local optima and accelerate convergence. By combining random search and pattern search as hybrid search operators, the algorithm’s local search capability is enhanced. The average population fitness is increased when predators engage in competitive learning behavior, which effectively promotes rapid convergence and greatly enhances solution quality. Simulations using 12 CEC2017 benchmark functions demonstrate that PSMPA achieves substantial improvements in optimization performance, convergence speed, and stability. Furthermore, its application in optimizing parameters for solar photovoltaic models further validates PSMPA’s practical value and effectiveness in solving real-world engineering optimization problems.