Photovoltaic (PV) array under partial shading conditions (PSCs) has several maximum power points (MPPs) in the power-voltage curve of the PV array. These points; have a unique global peak (GP) and the others are local peaks (LPs). This paper aims to study the application of a heuristic optimization technique namely, Invasive Weed Optimization (IWO) to track the global maximum power point (GMPP) of a PV array. The proposed Improved IWO (IIWO) algorithm modifies termination condition of the weed population to be faster and more efficient. In addition to study the effect of changing input parameters of IIWO on its performance. An overall statistical evaluation of IIWO, with standard IWO and Particle Swarm Optimization (PSO) is executed under different shading conditions. The simulation results show that IIWO has faster and better convergence as it can reach the GMPP in less iterations compared with other techniques. Six statistical metrics including mean absolute error, geometric mean error, the root mean square error, standard deviation, significance using t-test, and efficiency are used to estimate the superiority of the proposed IIWO. Hence, the proposed MPPT based on IIWO algorithm is considered to be the most efficient and outstanding optimization technique compared to other techniques.