Classic algorithms show high performance in tracking the maximum power point (MPP) of photovoltaic (PV) panels under uniform irradiance and temperature conditions. However, when partial or complex partial shading conditions occur, they fail in capturing the global maximum power point (GMPP) and are trapped in one of the local maximum power points (LMPPs) leading to a loss in power. On the other hand, intelligent algorithms inspired by nature show successful performance in GMPP tracking. In this study, an MPPT system was set up in MATLAB/Simulink software consisting of six groups of serially connected PV panels, a DC-DC boost converter, and load. Using this system, the cuckoo search (CS) algorithm, the modified incremental conductivity (MIC) algorithm, the particle swarm optimization (PSO) algorithm, and the grey wolf optimization (GWO) algorithm were compared in terms of productivity, convergence speed, efficiency, and oscillation under complex shading conditions. The results showed that the GWO algorithm showed superior performance compared to the other algorithms under complex shading conditions. It was observed that GWO did not oscillate during GMPP tracking with an average convergence speed of 0.22 s and a tracking efficiency of 99%. All these evaluations show that GWO is a very fast, highly accurate, efficient, and stable MPPT method under complex partial shading conditions.