Abstract
This paper proposes a novel nature-inspired metaheuristic algorithm, termed Kangaroo Escape Optimization (KEO) for accurate parameter extraction of photovoltaic (PV) models including the single-diode, double-diode, and triple-diode configurations. The algorithm simulates the survival-driven escape behavior of Kangaroos in uncertain environments, where each Kangaroo represents a candidate solution and its movement embodies the search for a safer zone, i.e., a better objective value. The suggested KEO incorporates a dual-phase exploration mechanism of zigzag motion and long-jump escape to diversify the search, governed by a chaotic logistic energy adaptation strategy. In the exploitation phase, Kangaroos adaptively choose either a random group member or the best among a nearby subset to guide local search, while a decoy drop mechanism refines convergence without premature stagnation. The switching between exploration and exploitation is regulated by a probabilistic model that ensures dynamic adaptability throughout iterations. The proposed KEO is assessed against state-of-the-art optimizers using the CEC 2022 benchmarks suite. Also, the study incorporates a comprehensive Confidence Interval (CI) analysis to assess robustness and conducts a sensitivity study on hyperparameters. Furthermore, the effectiveness of the proposed KEO approach is assessed using real-world current-voltage (I-V) datasets obtained from two benchmark PV modules: RTC France and Photowatt-PWP-201 PV modules. A detailed comparative study reveals that the KEO delivers superior performance relative to several optimization algorithms previously utilized for PV parameter identification. Specifically, KEO exhibits enhanced accuracy, robustness, and convergence efficiency when estimating the electrical parameters of solar cells across different equivalent circuit models. Moreover, the proposed KEO demonstrates significant performance under diverse irradiance and temperature conditions. The findings confirm KEO's capacity to reliably capture the complex nonlinear dynamics inherent in PV systems, positioning it as a versatile and powerful optimization tool for a broad range of renewable energy modeling tasks. The source code of THRO is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/181949-a-novel-kangaroo-escape-optimizer .