This paper proposes an accurate global maximum power point tracking (MPPT) technique based on Wind Driven Optimization (WDO) algorithm for photovoltaics system (PVS) under non-uniform solar irradiance. Under non-uniform solar irradiance or partial shading conditions (PSC), the power-voltage curve of the PVS has several maximum power points (MPPs), one of which is assumed to be the global peak. This condition complicates the optimal MPP tracking and decreases of the PVS efficiency. Conventional MPPT methods, such as Perturb & Observe, Hill Climbing and Incremental Inductance are likely to be trapped at one of the local MPPs. The major disadvantage of these methods is their incompetence to specify the global MPP under the above mentioned situation. To overcome this problem, global MPPT techniques based on modern optimization algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Harmony Search Algorithm (HSA), Bat Algorithm (Bat), Sine-Cosine Algorithm (SCA), Cuckoo Search (CS), and Genetic Algorithm (GA) are previously developed. The main contribution of this research is to introduce a novel global MPPT based on WDO algorithm. A comprehensive statistical evaluation of WDO, PSO, DE, HSA, Bat, SCA, CS and GA is carried out under different scenarios of shadowing conditions. Seven statistical metrics including the relative error, root mean square error, mean absolute error, standard deviation, success rate, convergence time and efficiency are used to evaluate the powerfulness and the feasibility of the proposed compared to the others. Therefore, the proposed MPPT based on WDO algorithm is considered to be the most effective and superior optimization tool compared to the corresponding ones.