Under partial shading condition, the power-voltage curve of the photovoltaic (PV) system contains several maximum power points (MPPs). Among these points, there is only single global and some local points. Accordingly, modern optimization algorithms are highly required to tackle this problem. However, the methods are considered as time consuming. Therefore, finding a new algorithm that capable to solve the problem of tracking global maximum power point (GMPP) with minimum number of population is highly appreciated. Several new straightforward methods as well as meta-heuristic approaches are exist. Recently, the Marine Predator Algorithm (MPA) has been developed for engineering applications. In this study, an alternative method of MPA, integrating Opposition Based Learning (OBL) strategy with Grey Wolf Optimizer (GWO), named MPAOBL-GWO, is proposed to cope with the implied weaknesses of classical MPA. Firstly, Opposition Based Learning (OBL) strategy is adopted to prevent MPA method from searching deflation and to obtain faster convergence rate. Besides, the GWO is also implemented to further improve the swarm agents’ local search efficiency. Due to that, the MPA explores the search space well better than exploiting it; so, this combination improves the efficiency of the MPA and avoids it from falling in local points. To verify the effectiveness of the enhanced method, the well-known CEC’17 test suite and the maximum power point tracking (MPPT) of photovoltaic (PV) system problem are solved. The obtained results illustrate the ability of the proposed MPAOBLGWO based method to achieve the optimum solution compared with the original MPA, GWO and Particle Swarm Optimization (PSO). The findings revealed that, the proposed method can be viewed as an efficient and effective strategy for more complex optimization scenarios and the MPPT as well.