Partial shading is an unavoidable severe problem experienced by a photovoltaic system. The P‐V characteristics are highly non‐linear and exhibit multiple peaks under partial shading conditions (PSC). The conventional MPPT techniques are unable to track global maximum power point (GMPP) under PSC. Various bio‐inspired optimization methods employed for MPPT were presented in the literature. The operation of each algorithm differs from one another when tracking the GMPP. Therefore, this paper proposes a hybrid of adaptive particle swarm optimization (APSO) and Nelder Mead (NM) in searching for the global maximum power point. In this hybrid method, the unnecessary movement of APSO particles is minimized, leading to rapid convergence without the local trapping of the NM process. Thus, the proposed algorithm can balance both explorations by APSO and exploitation by NM. Extensive simulations are performed using Matlab/Simulink, which shows that this method successfully tracks global power under various shading patterns. The proposed method is compared with existing advanced optimization algorithms. Further validation is done via experimental analysis, and it confirms that the proposed method performs better in tracking maximum global power, less steady‐state oscillations, and fast‐tracking speed. The results demonstrate the effectiveness of proposed method in tracking GMPP with an average efficiency of 99.32% and an average tracking time of 0.513 s.

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