<![CDATA[Maximum Power Point Tracking (MPPT) has been proven to improve power extraction in photovoltaic (PV) systems. However, conventional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (InC) have limitations, such as oscillations in steady state conditions, slow response, and a tendency to get stuck at local maxima when irradiation changes. This study aims to evaluate biology-inspired metaheuristic algorithms to improve tracking accuracy, convergence speed, and MPPT stability in PV systems. These algorithms include Grey Wolf Optimization (GWO), Sand Cat Swarm Optimization (SCSO), Horse Herd Optimization (HHO), Chameleon Swarm Algorithm (CSA), and Flying Squirrel Search Optimization (FSSO). The algorithms were tested using the same general parameters to ensure a fair comparison. Testing was conducted on PV models, DC boost converters with resistive loads and batteries under static and dynamic irradiation conditions using MATLAB/Simulink. The results show that HHO provides the best performance with an efficiency of 99.96% at 1000 W/m² and 98.03% at 800 W/m², a tracking time of <0.05 seconds, and power fluctuations of <0.3% in resistive load testing. In battery testing, CSA and FSSO showed the best performance with voltage stability, high charging current, and lower ripple. Overall, the results of this study indicate that the proposed metaheuristic-based MPPT algorithm can improve the accuracy of maximum power point tracking, accelerate convergence time, and minimize power oscillations in PV systems.]]>
