Comparative Analysis of Nature-Inspired Optimization Algorithms for Partial Shading Conditions

Photovoltaic (PV) systems have gained significant attention as a sustainable energy source, but their efficiency is highly dependent on environmental conditions. Under Partial Shading Conditions (PSC), multiple power peaks occur, making conventional Maximum Power Point Tracking (MPPT) techniques ineffective. To address this, several nature-inspired optimization algorithms have been developed, including Particle Swarm Optimization (PSO), Adaptive PSO, Cuckoo Search (CS), Flower Pollination Algorithm (FPA), Grey Wolf Optimizer (GWO), Horse Herd Optimization (HHO), and Hybrid PO-PSO. This study presents a comparative analysis of these algorithms in terms of tracking efficiency and robustness under dynamic shading patterns. PSO and its adaptive variant show fast convergence but may suffer from local optima. CS and FPA offer improved exploration capabilities, whereas GWO and HHO demonstrate better stability in complex landscapes. The Hybrid PO-PSO approach integrates the benefits of PO and PSO, achieving enhanced performance. Simulation results validate the effectiveness of each algorithm in extracting the maximum available power under different PSC scenarios. The analysis provides insights into the optimal selection of MPPT techniques for improving the reliability and efficiency of PV systems.