<![CDATA[Photovoltaic efficiency is frequently compromised by physical obstructions, resulting in partial shading conditions. This non-uniform irradiance condition severely distorts system characteristics by inducing multiple power peaks. This study proposes a novel fractional tent map-chaotic horse herd optimization (FTM-CHHO) algorithm for global maximum power point (GMPP). By integrating fractional-order memory and chaotic maps, FTM-CHHO enhances global search capabilities and prevents entrapment in local maxima. The method was rigorously validated through simulations and hardware experiments using a SEPIC converter. Simulations demonstrated that FTM-CHHO achieved 99.52 % to 100 % tracking accuracy with rapid convergence times of 0.32 to 0.62 s. Furthermore, hardware tests under real-world shading confirmed its robustness, maintaining 95.54 % to 98.26 % accuracy and converging within 10.1 s. FTM-CHHO significantly outperformed perturb and observe (P8O) and standard horse herd optimization (HHO). These findings confirm that FTM-CHHO provides a highly reliable, fast, and efficient solution for maximizing solar energy extraction under complex environmental variability.]]>
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