A Survey of MPPT Techniques Under Partial Shading Conditions: Evaluating the Firefly Algorithm's Performance

Partial shading conditions (PSC) pose significant challenges to the efficient operation of photovoltaic (PV) systems, as they create multiple local maxima in the powervoltage (P-V) curve, often misleading conventional Maximum Power Point Tracking (MPPT) algorithms. To address this, metaheuristic techniques have gained attention for their ability to identify the global maximum power point (GMPP) under such complex conditions. This paper provides a focused state-of-the-art review of MPPT methods, with particular emphasis on the Firefly Algorithm (FA) and its application in PV systems operating under PSC. The FA, inspired by the flashing behavior of fireflies, is examined in terms of its core mechanisms—light intensity, attractiveness, and randomness—and how these enable effective GMPP tracking. A critical review of recent studies is presented, analyzing FA-based MPPT approaches in terms of tracking accuracy, convergence speed, energy efficiency, and computational complexity. Hybrid techniques combining FA with other algorithms are also discussed, highlighting enhanced performance in dynamic shading scenarios. Additionally, a bibliometric analysis using VOSviewer was conducted to visualize research trends, coauthorship networks, and thematic clusters within this field. The study concludes by identifying key research gaps and future directions for optimizing FA-based MPPT strategies in partially shaded PV systems.