An Improved Elk Herd Optimization Algorithm for Maximum Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

In partial shading conditions (PSCs), the power–voltage characteristics of photovoltaic systems exhibit multiple peaks, causing traditional maximum power point tracking (MPPT) algorithms to easily become trapped in local optima and fail to achieve global maximum power point tracking, thereby reducing energy conversion efficiency. Effectively and rapidly locating the global maximum power under complex environmental conditions has become crucial for enhancing MPPT performance in photovoltaic systems. This paper therefore proposes an improved elk herd optimization (IEHO) algorithm to achieve the rapid tracking of the global maximum power point under various weather conditions. The algorithm proposes a position update mechanism guided by the predation risk probability to direct elk herd migration and introduces the triangle walk strategy, thereby enhancing the algorithm’s capability to avoid local optima. Furthermore, IEHO employs a memory-guided redirection strategy to skip redundant calculations of historical duty cycles, significantly improving the convergence speed of MPPT. To validate the algorithm’s performance advantages, the proposed IEHO method is compared with other recognized meta-heuristic algorithms under various weather conditions. The experimental results demonstrate that, across all tested conditions, the proposed IEHO method achieves an average tracking efficiency of 99.99% and an average tracking time of 0.3886 s, outperforming other comparative algorithms.