Solar systems connected to the grid are crucial in addressing the global energy crisis and meeting rising energy demand. The efficiency of these systems is totally impacted by varying weather conditions such as changes in irradiance and temperature throughout the day. Additionally, partial shading (PS) adds to the complexity of the nonlinear characteristics of photovoltaic (PV) systems, leading to significant power loss. To address this issue, maximum power point tracking (MPPT) algorithms have become an essential component in PV systems to ensure optimal power extraction. This paper introduces a new MPPT control technique based on a novel reptile search optimization algorithm (RSA). The effectiveness of the proposed RSA method is evaluated under different weather conditions with varying irradiance and partial shading. The results of the RSA algorithm are compared to other existing bio-inspired algorithms and show superior performance with an average efficiency of 99.91%, faster dynamic response of 50 ms, and less than 20 watts of oscillation. The RSA-MPPT based technique provides higher efficiency, faster settling time, and minimal oscillation around the maximum power point (MPP), making it a reliable solution for effective solar power harvesting.

Proton exchange membrane fuel cells (PEMFCs) represent a promising renewable energy technology that converts chemical energy from hydrogen and oxygen into electrical energy. Accurate mathematical modeling and precise parameter identification are essential for optimizing PEMFC performance and control. This study proposes a novel hybrid meta-heuristic algorithm, the mutated puma optimizer (Mu-PO), which integrates a mutation operator from differential evolution to enhance the exploration and exploitation capabilities of the conventional puma optimizer, enabling it to escape local minima and reach global optima in fewer iterations. A sum of squared error (SSE)-based objective function is formulated to minimize the discrepancy between estimated and experimental voltages. The proposed method identifies seven unknown parameters for three commercial PEMFC models (250 W, SR-12, and NedStack PS6), achieving SSE values of 0.6419, 1.0566, and 2.0791, respectively. Notably, Mu-PO attains these low SSE values in fewer than 50 iterations for all models, demonstrating rapid convergence. Comparative analysis using statistical indicators (minimum, mean, maximum, and standard deviation of SSE) confirms that Mu-PO outperforms well-established optimization algorithms in terms of convergence speed, stability, and accuracy. Furthermore, validation under dynamic operating conditions, including variations in pressure and temperature, demonstrates consistent and reliable parameter identification, highlighting the robustness and practical applicability of the proposed approach for PEMFC modeling and optimization.

Accurate identification of photovoltaic (PV) cell and module parameters remains a fundamental yet challenging task, particularly as model complexity increases from five to nine unknown parameters. In this study, the parameter extraction problem is rigorously formulated as a nonlinear optimization task and addressed using a novel hybrid metaheuristic algorithm, termed the Shuffled Frog Leaping–Shuffled Complex Evolution (SFL-SCE) method. The proposed approach synergistically integrates the population-based social learning mechanism of the Shuffled Frog Leaping Algorithm (SFL) with the robust global search and refinement capabilities of Shuffled Complex Evolution (SCE), thereby achieving an effective balance between exploration and exploitation. The SFL-SCE algorithm minimizes the root-mean-square error (RMSE) between measured and simulated current–voltage characteristics and is systematically applied to three widely used PV technologies: the RTC-France silicon solar cell, the polycrystalline Photowatt-PWP201 module, and the monocrystalline STM6-40/36 module. For each device, parameter identification is performed under one-diode, two-diode, and three-diode modelling frameworks, encompassing increasing levels of physical fidelity and computational complexity. Experimental data are employed throughout to ensure practical relevance and robustness. The performance of the proposed algorithm is comprehensively evaluated against its constituent algorithms (SFLA and SCE) as well as several state-of-the-art hybrid optimization techniques reported in the literature. Comparative results demonstrate that SFL-SCE consistently achieves superior accuracy, enhanced reliability, and faster convergence, as evidenced by lower minimum, mean, and maximum RMSE values, reduced standard deviation, and improved convergence behavior across all test cases. These findings confirm the effectiveness of the proposed hybridization strategy and establish SFL-SCE as a powerful and reliable tool for high-precision PV model parameter identification.

This article is an in-depth analysis of the performance and efficiency of various control systems used in quadrotor unmanned aerial vehicles (UAVs). The study is focused on the comparison of three main control approaches, including Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and an extended version of Sliding Mode Control with the use of the Gray Wolf Optimizer (SMC-GWO), as well as a supportive validation model the Genetic Algorithm (SMC-GA). Based on the Newton–Euler formulation, the mathematical model of a quadrotor has been developed to provide a true picture of the dynamic behavior of the quadrotor. The model was then implemented in MATLAB/Simulink 2025b to test the performance of the system in its nominal and perturbed conditions. The findings have shown that the hybrid SMC-GWO controller has significant improvement in response speed, accuracy, and stability compared to the other controllers. Precisely, the SMC-GWO demonstrated 78.46 percent decrease in rise time and 23.40 percent decrease in settling time compared to the traditional SMC, as well as a nearly negligible steady-state error (SSE = 0.0008) in the roll channel. The proposed controller in the pitch channel reduced the rise time by 93.65 percent and the settling time by 20.22 percent, with a much smoother and more stable tracking and an effectively negligible steady-state error (SSE = 0.0001). The hybrid controller in the yaw channel had a 77.94 percent better rise time and 23.16 percent better settling time, resulting in a steady-state error of 0.0022. In relation to altitude control, SMC-GWO decreased the rise time by 91.87 percent and settling time by 25.04 percent over classical SMC, yet the steady-state error was almost zero. Under constant, time-varying actuator disturbances, the SMC-GWO controller also demonstrated better system stabilization and trajectory-tracking behavior than both SMC and FLC, as well as slightly better behavior than SMC-GA in the presence of faults and disturbances. These results verify that a UAV control framework based on the combination of the Gray Wolf Optimizer and Sliding Mode Control is more resilient, quick, and significantly more precise.