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This paper introduces an Improved Rime Optimization Algorithm (IROA) designed to maximize achievable rates in multiuser wireless communication networks equipped with Reconfigurable intelligent surfaces (RISs). The proposed technique incorporates the Quadratic Interpolation Method (QIM) into the classic Rime Optimization Algorithm (ROA), which improves solution diversity, facilitates broader exploration of the search space, and enhances robustness against local optima. Finding the ideal quantity and positioning of RIS components to optimize system performance is the main goal of the optimization framework. Two objective models are taken into consideration: one that maximizes the lowest achievable rate in order to prioritize fairness, and another that maximizes the average achievable rate for all users. The performance of IROA is evaluated on systems with 20 and 50 users and compared against established algorithms such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Augmented Jellyfish Search Optimization Algorithm (AJFSOA), and Jellyfish Search Optimization Algorithm (JFSOA). Results demonstrate that the proposed IROA achieves relative performance improvements ranging from 5% to 46% across different scenarios and objective models. In the 20-user case with the first objective model, IROA achieves improvements of 28.02%, 42.07%, 46.54%, 1.74%, 35.46%, and 25.95% compared to AJFSOA, JFSOA, PSO, ROA, GWO, and DE, respectively, in terms of average achievable rate. Similarly, for the second objective model, IROA achieves relative improvements of 5.94%, 13.29%, 14.55%, 7.1%, 15.97%, and 46.26% over ROA, DE, PSO, AJFSOA, JFSOA, and GWO, respectively, in terms of minimum achievable rate. On contrary, the IROA shows lower standard deviation compared to the current ROA. However, the proposed IROA achieves superior performance over ROA in terms of the best, mean and worst objective outcomes. These findings demonstrate that in RIS-assisted wireless communication networks, the suggested IROA achieves strong flexibility and reliable performance benefits across a range of multiuser optimization tasks.

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 .

Sustainable energy management hinges on precise forecasting of renewable energy sources, with a specific focus on solar power. To enhance resource allocation and grid integration, this study introduces an innovative hybrid approach that integrates meteorological data into prediction models for photovoltaic (PV) power generation. A thorough analysis is performed utilizing the Desert Knowledge Australia Solar Centre (DKASC) Hanwha Solar dataset encompassing PV output power and meteorological variables from sensors. The aim is to develop a distinctive hybrid predictive model framework by integrating feature selection techniques with various regression algorithms. This model, referred to as the PV power generation predictive model (PVPGPM), utilizes meteorological data specific to the DKASC. In this study, various feature selection techniques are implemented, including Pearson correlation (PC), variance inflation factor (VIF), mutual information (MI), step forward selection (SFS), backward elimination (BE), recursive feature elimination (RFE), and embedded method (EM), to identify the most influential factors for PV power prediction. Furthermore, a hybrid predictive model integrating multiple regression algorithms is introduced, including linear regression, ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Elastic Net, Extra Trees Regressor, random forest regressor, gradient boosting (GB) regressor, eXtreme Gradient Boosting (XGBoost) Regressor, and a hybrid model thereof. Extensive experimentation and evaluation showcase the effectiveness of the proposed approach in achieving high prediction accuracy. Results demonstrate that the hybrid model comprising XGBoost Regressor, Extra Trees Regressor, and GB regressor surpasses other regression algorithms, yielding a minimal root mean square error (RMSE) of 0.108735 and the highest R ‐squared ( R 2 ) value of 0.996228. The findings underscore the importance of integrating meteorological insights into renewable energy forecasting for sustainable energy planning and management.

The parameter extraction of the proton exchange membrane fuel cells (PEMFCs) is an active study area over the past few years to achieve accurate current/voltage (I/V) curves. This work proposes an advanced version of an improved gorilla troops technique (IGTT) to precisely estimate the PEMFC’s model parameters. The GTT's dual implementation of the migration approach enables boosting the exploitation phase and preventing becoming trapped in the local minima. Besides, a Tangent Flight Strategy (TFS) is incorporated with the exploitation stage for efficiently searching the search space. Using two common PEMFCs stacks of BCS 500W, and Modular SR-12, the developed IGTT is effectively applied. Furthermore, the two models are evaluated under varied partial temperature and pressure. In addition to this, different new recently inspired optimizers are employed for comparative validations namely supply demand optimization (SDO), flying foxes optimizer (FFO) and red fox optimizer (RFO). Also, a comparative assessment of the developed IGTT and the original GTT are tested to ten unconstrained benchmark functions following to the Congress on Evolutionary Computation (CEC) 2017. The proposed IGTT outperforms the standard GTT, grey wolf algorithm (GWA) and Particle swarm optimizer (PSO) in 92.5%, 87.5% and 92.5% of the statistical indices. Moreover, the viability of the IGTT is proved in comparison to various previously published frameworks-based parameter's identification of PEMFCs stacks. The obtained sum of squared errors (SSE) and the standard deviations (STD) are among the difficult approaches in this context and are quite competitive. For the PEMFCs stacks being studied, the developed IGTT achieves exceedingly small SSE values of 0.0117 and 0.000142 for BCS 500 and SR-12, respectively. Added to that, the IGTT gives superior performance compared to GTT, SDO, FFO and RFO obtaining the smallest SSE objective with the least STD ever.

The parameter extraction of PV models is a nonlinear and multi-model optimization problem. However, it is essential to correctly estimate the parameters of the PV units due to their impact on the PV system efficiency in terms of power and current production. As a result, this study introduces a developed Artificial Hummingbird Technique (AHT) to generate the best values of the ungiven parameters of these PV units. The AHT mimics hummingbirds' unique flying abilities and foraging methods in the wild. The AHT is compared with numerous recent inspired techniques which are tuna swarm optimizer, African vulture’s optimizer, teaching learning studying-based optimizer and other recent optimization techniques. The statistical studies and experimental findings show that AHT outperforms other methods in extracting the parameters of various PV models of STM6-40/36, KC200GT and PWP 201 polycrystalline. The AHT’s performance is evaluated using the datasheet provided by the manufacturer. To highlight the AHT dominance, its performance is compared to those of other competing techniques. The simulation outcomes demonstrate that the AHT algorithm features a quick processing time and steadily convergence in consort with keeping an elevated level of accuracy in the offered solution.

Fuel cells (FCs) have garnered significant attention due to their versatile applications, but their nonlinear characteristics pose challenges in the modeling process. This research presents a unique enhanced artificial hummingbird algorithm (EAHA) aimed at identifying the seven unknown parameters of the proton exchange membrane fuel cells (PEMFCs) stack by utilizing their experimental datasets. To accomplish this, the objective is to achieve accurate current/voltage ( I / V ) curves where a cost function is defined using the aggregation of quadratic deviations (AQD) between the measured dataset points and the appropriate model‐based estimations. The presented EAHA combines several territorial foraging techniques with a linear regulating mechanism. The performance of the conventional AHA is compared with the suggested EAHA using three commonly employed PEMFC modules. Furthermore, a comparative analysis is conducted against previously published methodologies and newly developed optimizers such as the equilibrium optimizer (EO), social networking search (SNS) technique, slim mold algorithm (SMA), heap‐based optimizer (HBO), and African vultures optimization (AVO) technique. The findings are compared to existing methodologies and other state‐of‐the‐art optimizers, providing valuable insights into the efficacy of the proposed approach. For the 250 W PEMFC stack, the proposed EAHA shows improvements of 2.966%, 6.493%, 1.491%, 7.080%, 1.131%, and 2.875% over AHA, AVO, EO, HBO, SMA, and SNS, respectively, depending on the mean AQD values. Similar findings are attained for the other two stacks. For example, for the test case of the BCS 500 W PEMFCs stack, the proposed EAHA demonstrates improvements of 64.228%, 82.859%, 66.140%, 81.156%, 46.302%, and 71.635% over AHA, AVO, EO, HBO, SMA, and SNS, respectively.

This study introduces an enhanced version of quadratic interpolation optimization (QIO) merged with Gaussian mutation (GM) operator for optimizing photovoltaic (PV) units and capacitors within distribution systems, addressing practical considerations and discrete nature of capacitors. In this regard, the variations in power loading and power productions from PV sources are taken into consideration. The QIO is inspired by the generalized quadratic interpolation (GQI) method in mathematics and is enhanced with GM operator that introduces randomness into the solution to explore the search space and avoid premature convergence. The proposed QIO‐GM is tested on practical Egyptian and standard IEEE distribution systems, demonstrating its effectiveness in minimizing energy losses. Comparative studies against standard QIO, northern goshawk optimization (NGO), and optical microscope algorithm (OMA), as well as other reported algorithms, validate QIO‐GM’s superior performance. Numerically, in the first system, the designed QIO‐GM algorithm achieves 2.5% improvement over QIO, a 4.4% improvement over NGO, and a 9.2% improvement over OMA, leading to a substantial reduction in carbon dioxide (Co 2 ) emissions from 110,823.886 to 79,402.82 kg, reflecting a commendable 28.35% decrease. Similarly, in the second system, QIO demonstrates a significant reduction in Co 2 emissions from 72,283.328 to 54,627.65 kg, with a commendable 28.3% decrease. These results underscore QIO‐GM’s effectiveness in not only optimizing energy losses but also contributing to substantial environmental benefits through reduced emissions.

Accurate parameter estimation in photovoltaic (PV) models is essential for optimizing solar energy systems, enhancing their efficiency, and ensuring precise performance predictions. This paper proposes a novel Improved version of Rime Metaheuristic Optimization (RMO) influenced by rime growth and combined with Quadratic Interpolation Learning (QIL) technique for the simulation and design of the triple-Diode Model (DM). This novel combination seeks to provide a more accurate perspective in the field of solar energy optimization by managing the complexities of PV module characterization with greater flexibility and resilience. By meticulously replicating the distinctive features of both processes, the hard-rime puncture and soft-rime searching are disclosed. The QIL technique improves the search process by selecting three different rime particles rather than relying solely on the current best solution. This selection allows for a more diverse set of candidate solutions, fostering better exploration and reducing premature convergence to local optima. By leveraging quadratic interpolation, QIL adjusts the solution updates in a flexible and nonlinear manner, enabling a more precise and adaptive parameter estimation process. QIL’s capacity to adjust its quadratic function in a flexible and non-linear way makes it easier to navigate complex terrain. The novel IRMO as well as the original RMO are developed for predicting PV parameters for the triple-diode model (DM) of the three distinct PV modules which are Photowatt PWP201, STM6-40/36, and R.T.C France. In accordance with other published publications, the results of the suggested IRMO are also compared with those of contemporary algorithms. According to the results of the simulation, the upgraded IRMO shows significant average improvements of 49.56%, 62.56%, and 34.15% for the three modules, correspondingly.

Electricity drives economic growth, technological advancement, and improved quality of life, but it also poses environmental pollution challenges due to reliance on traditional energy sources such as petroleum and natural gas. Distribution systems’ extensive reach makes it easier to integrate different renewable energies, particularly solar power, across different voltage levels. While integrating solar photovoltaic (PV) cells into existing traditional distribution systems may seem straightforward, studies reveal that their unchecked proliferation can lead to increased electrical losses and greater disruptions in power quality. This study introduces a coordinated methodology of PV energy systems and capacitor bank (CB) devices in electrical distribution feeders. The presented coordinated integration offers a sustainable energy solution for mitigating system losses, facilitating voltage profile enhancement as an important power quality indicator for adequate customer operation. In this regard, practical concerns include variations in power loadings, the discrete nature of CBs, and actual power production from PV sources are taken into consideration. For handling the presented coordinated integration, this paper develops the electric eel foraging-based optimization (EEFO) for energy efficiency and power quality improvement as well as environmental sustainability. The designed EEFO has been evaluated on practical Egyptian and standard IEEE distribution systems, demonstrating its effectiveness in minimizing energy losses and improving power quality. Comparative studies against reported algorithms validate EEFO’s superior performance.

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger’s sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario.