Optimal parameter extraction of fuel cells based on interval branch-and-bound optimization algorithm

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.

Equality graphs (e-graphs) are used to compactly represent equivalence classes of terms in symbolic reasoning systems. Beyond their original roots in automated theorem proving, e-graphs have been used in a variety of applications. They have become particularly important as the key ingredient in the popular technique of equality saturation, which has notable applications in compiler optimization, program synthesis, program verification, and symbolic execution, among others. In a typical equality saturation workflow, an e-graph is used to store a large number of equalities that are generated by local rewrites during a saturation phase, after which an optimal term is extracted from the e-graph as the output of the technique. However, despite its crucial role in equality saturation, e-graph extraction has received relatively little attention in the literature, which we seek to start addressing in this paper. Extraction is a challenging problem and is notably known to be NP-hard in general, so current equality saturation tools rely either on slow optimal extraction algorithms based on integer linear programming (ILP) or on heuristics that may not always produce the optimal result. In fact, in this paper, we show that e-graph extraction is hard to approximate within any constant ratio. Thus, any such heuristic will produce wildly suboptimal results in the worst case. Fortunately, we show that the problem becomes tractable when the e-graph is sparse, which is the case in many practical applications. We present a novel parameterized algorithm for extracting optimal terms from e-graphs with low treewidth, a measure of how “tree-like” a graph is, and prove its correctness. We also present an efficient Rust implementation of our algorithm and evaluate it against ILP on a number of benchmarks extracted from the Cranelift benchmark suite, a real-world compiler optimization library based on equality saturation. Our algorithm optimally extracts e-graphs with treewidths of up to 10 in a fraction of the time taken by ILP. These results suggest that our algorithm can be a valuable tool for equality saturation users who need to extract optimal terms from sparse e-graphs.

Signature files seem to be a promising access method for text and attributes. According to this method, the documents (or records) are stored sequentially in one file ("text file"), while abstractions of the documents ("signatures") are stored sequentially in another file ("signature file"). In order to resolve a query, the signature file is scanned first, and many nonqualifying documents are immediately rejected. We develop a framework that includes primary key hashing, multiattribute hashing, and signature files. Our effort is to find the optimal signature extraction method. The main contribution of this paper is that we present optimal and efficient suboptimal algorithms for assigning words to signatures in several environments. Another contribution is that we use information theory, and study the relationship of the false drop probability F d and the information that is lost during signature extraction. We give tight lower bounds on the achievable F d and show that a simple relationship holds between the two quantities in the case of optimal signature extraction with uniform occurrence and query frequencies. We examine hashing as a method to map words to signatures (instead of the optimal way), and show that the same relationship holds between F d and loss , indicating that an invariant may exist between these two quantities for every signature extraction method.

Accurate and efficient compact model parameter extraction is critical for integrated circuit design using advanced transistor architectures. In this work, we benchmark four widely used optimization algorithms– Differential Evolution (DE), Particle Swarm Optimization (PSO) and Bayesian Optimization (BO)–for BSIM-CMG parameter extraction in nanosheet FETs. Each optimizer is evaluated in both conventional (non-dynamic) and dynamic configurations, where the latter incorporates staged hyperparameter adaptation to balance exploration and exploitation. Benchmarking demonstrates that dynamic algorithms consistently outperform their non-dynamic counterparts, with DE-Dynamic exhibiting the highest consistency and lowest variance across multiple runs. This benchmarking study identifies the most suitable optimization strategies for compact model parameter calibration and underscore the significant advantages of dynamic hyperparameter adaptation, offering a more reliable and efficient approach to automated parameter extraction for complex compact models.

Parameter extraction for photodiode equivalent circuit model based on hybrid genetic algorithm

The speed of urbanization around the world is decreasing the arable land endangering food security since the population is estimated to reach 9.7 billion by the year 2050. Urban agriculture provides a long-term solution to food production in urban areas but has issues of good monitoring of plant diseases because growing areas are fragmented, microclimates change, and resources are limited. However, biotic (e.g., pathogens) and abiotic stresses must be accurately detected to reduce wastage in crop and ensure sustainability in urban farming ecosystems. This paper will suggest a new deep learning model that integrates ResNet101 and the Sparrow Search Optimization (SSO) algorithm to identify plant stress in urban agriculture environments. Based on the capabilities of transfer learning, the model makes use of optimal feature extraction with small datasets, resolving the issue of data scarcity in cities. The framework was trained and evaluated based on a heterogeneous dataset of urban crop images, inclusive of multifactorial stress indicators on variable conditions. ResNet101 + SSO reached an F1-score of 98.9, and ROC-AUC of 0.989, which is better than the traditional approaches (RandomForest: 92.3% F1; KNN 89.7% F1). It showed great accuracy in detecting both biotic and abiotic stress factors, which allows the timely detection of the broken urban farms. This solution promotes sustainable urban agriculture by minimizing the waste of crops by monitoring stress accurately and at scale. The model is developed to support smart city objectives of improving food security and resources sustainability, which is tailored to city settings with limited resources. The future planning of work will be to combine real-time data of IoT sensors and make the model applicable to various types of crops used in urban areas.

Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature extraction algorithms namely Principle Component Analysis (PCA), Sparse PCA, Kernel PCA and Incremental PCA. These algorithms are integrated with machine learning models to improve HF prediction. The performance of all these integrated models are evaluated by analyzing Cleveland heart failure database. Experimental results pointed out that Kernel PCA algorithm integrated with linear discriminant analysis model and Sparse PCA integrated with Gaussian Naive Bayes (GNB) model offers 91.11% of HF classification accuracy. Hence, based on the experimental results it is shown that Kernel PCA and Sparse PCA are suitable feature extraction methods for HF data.

Iris Feature Extraction Algorithm Evaluation is an important link of evaluation on iris identification system.For the need of optimal feature extraction algorithm according to the identification, this thesis provides an evaluation scheme based on the fuzzy integral model, and builds a relevant evaluation model, which proves to make a scientific evaluation on the algorithm in the phase of feature extraction.

Multi-source systems are gaining attention as an effective approach to seamlessly incorporate renewable energies within electrical networks. These systems offer greater flexibility and better energy management possibilities. The considered multi-source system is based on a 50 MW wind farm connected to battery energy storage and electrolyzers through modular multi-level DC/DC converters. Wind energy systems interface with the DC-bus via rectifier power electronics that regulate the DC-bus voltage and implement optimal power extraction algorithms for efficient wind turbine operation. However, integrating intermittent renewable energy sources with optimal microgrid management poses significant challenges. It is essential to mention that the studied multi-source system is connected to the DC loads (modular electrolyzers and local load). This work proposes a new regulation method designed specifically to improve the performance of the system. In this strategy, the excess wind farm energy is converted into hydrogen gas and may be stored in the batteries. On the other hand, when the wind speed is low or there is no excess of energy, electrolyzer operations are stopped. The battery energy management depends on the power balance between the DC load (modular electrolyzers and local load) requirements and the energy produced from the wind farm. This control should lead to eliminating the fluctuations in energy production and should have a high dynamic performance. This work presents a nonlinear control method using a backstepping concept to improve the performances of the system operations and to achieve the mentioned goals. To evaluate the developed control strategy, some simulations based on real meteorological wind speed data using Matlab are conducted. The simulation results show that the proposed backstepping control strategy is satisfactory. Indeed, by integrating this control strategy into the multi-source system, we offer a flexible solution for battery and electrolyzer applications, contributing to the transition to a cleaner, more resilient energy system. This methodology offers intelligent and efficient energy management.

The classification of unstructured P2P multicast video streaming is the premise for playing online linkage and real-time evidence in the process of network monitoring management. Based on the classification method in the preliminary research, an improved classification method is proposed. the method uses an optimal feature vector extraction algorithm to filter the proposed behavior features in the original method, which greatly lower the feature vector dimension. an improved multi-class support vector machines is also used in the improved method, which not only solves the problem of rejecting sub-regional, but also reduces the computational complexity and improves the identification accuracy. Experiments show that the improved method has less computational and storage overhead, and has higher identification accuracy.

This research proposes a novel approach for accurate parameter extraction of photovoltaic (PV) modules using the Smell Agent Optimization (SAO) algorithm. Accurate PV parameter estimation is essential for reliable modeling, performance optimization, and effective integration into renewable energy systems. The proposed SAO-based method is inspired by agent–environment interactions and is designed to improve estimation accuracy while maintaining computational efficiency. A comprehensive review of existing parameter extraction techniques is conducted to identify limitations related to accuracy and convergence behavior. Based on this analysis, the SAO algorithm is implemented to estimate key PV parameters, including short-circuit current, open-circuit voltage, maximum power point current, and maximum power point voltage. The proposed method is validated through simulations using real performance data from a selected PV module. Results demonstrate a substantial reduction in absolute errors, with current and power extraction errors of 0.06 A and 0.03 W, respectively, representing an improvement of approximately 15% compared to conventional optimization methods. Additionally, the robustness of the SAO algorithm is evaluated under varying environmental conditions, confirming its adaptability and consistent performance. The findings indicate that SAO provides a reliable and efficient solution for PV module parameter extraction.

This paper proposes the use of genetic algorithms for process optimization and calibration of model parameters. The main principles of these evolution inspired optimizers are briefly explained. Afterwards, their application to two process simulation tasks is presented: (1) a layout problem in lithography processes and (2) the extraction of physical parameters of a diffusion model. The obtained results are shortly discussed.KeywordsGenetic AlgorithmProcess WindowLayout ProblemFraunhofer InstituteOptical LithographyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Isoflavones, particularly puerarin, are a group of important components from Puerariae Lobatae Radix (PLR) which show diverse pharmacological activities, therefore giving rise to the development of various extraction methods. Ultrasound-assisted extraction (UAE) has been explored for isoflavone extraction from PLR as it provides higher extraction efficiency compared with traditional methods. To optimise the UAE conditions for puerarin and total isoflavone extraction from PLR using response surface methodology (RSM). Samples were prepared by use of ultrasound-assisted extraction, and then subjected to HPLC analysis. Box-Behnken Design (BBD), a widely used form of RSM, was used for the optimisation of the UAE process. The effects of ethanol concentration, extraction time and the solvent-to-material ratio on the yields of puerarin and total isoflavones were investigated. The Box-Behnken experimental results demonstrate that optimal extraction was obtained with an ethanol concentration of 71.35%, an extraction time of 49.08 min and a solvent-to-material ratio of 21.72 for puerarin, and an ethanol concentration of 80.00%, an extraction time of 55.00 min and a solvent-to-material ratio of 12.81 for total isoflavones. The yields of puerarin and total isoflavones were 41 ± 0.63 mg/g and 128 ± 0.82 mg/g, respectively, under the optimised extraction conditions, which are in agreement with the values predicted by the RSM. The RSM allows for optimising the extraction parameters such that maximum extraction of puerarin and total isoflavones was achieved experimentally.

The critical worldwide revolution towards clean energy has prompted the improvement of studies on the fabrication of high-performance solar cells. In this regard, rendering an accurate model of the solar cell for performance evaluation in the simulation could be essential. So far, several models have been proposed for the solar cell, including single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). By increasing the number of diodes considered in the equivalent circuit in order to deliver a more accurate model, the number of unknown parameters which must be identified will be increased as well. Therefore, presenting an efficient algorithm to estimate these parameters becomes an interesting issue in recent years. In this study, an improved optimization algorithm, called springy whale optimization algorithm (SWOA), is proposed to estimate the model parameters of solar cells. SWOA is a generalization of the WOA and has the advantages of high convergence speed, global search capability, and high robustness over it. In order to inquire the efficiency of SWOA, this algorithm is posed to estimate the parameters of models of solar cells and photovoltaic (PV) modules as well; the simulation results authenticate the supremacy of the proposed algorithm. Furthermore, the effectiveness of SWOA algorithm in the practical application has been evaluated using commercial modules, including polycrystalline (SW255), multi-crystalline (KC200GT), and monocrystalline (SM55). This assessment is carried out for various operating conditions under different irradiance and temperature conditions, which yield variations in the parameters of the PV model. The results obtained from various experimental setups confirm the high performance and robustness of the proposed algorithm.

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications.