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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

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PeerJ computer Science 2025年 11卷 e2646-e2646页

作者： Zga, Adel Zitouni, Farouq Harous, Saad Sallam, Karam Almazyad, Abdulaziz S. Xiong, Guojiang Mohamed, Ali Wagdy Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

This study conducts a comparative analysis of the performance of ten novel and wellperforming metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the singlediode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triplediode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its

关键词： Mean square error

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Data-Driven Learning and Control with Event-Triggered Measurements

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IEEE Transactions on Automatic Control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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A Stackelberg Game Approach for Signal Temporal Logic Control Synthesis with Uncontrollable Agents

arXiv

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arXiv 2025年

作者： Cui, Bohan Yu, Xinyi Giua, Alessandro Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Thomas Lord Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Electrical and Electronic Engineering University of Cagliari Cagliari09123 Italy

In this paper, we investigate the control synthesis problem for Signal Temporal Logic (STL) specifications in the presence of uncontrollable agents. Existing works mainly address this problem in a robust control setting by assuming the uncontrollable agents are adversarial and accounting for the worst-case scenario. While this approach ensures safety, it can be overly conservative in scenarios where uncontrollable agents have their own objectives that are not entirely opposed to the system’s goals. Motivated by this limitation, we propose a new framework for STL control synthesis within the Stackelberg game setting. Specifically, we assume that the system controller, acting as the leader, first commits to a plan, after which the uncontrollable agents, acting as followers, take a best response based on the committed plan and their own objectives. Our goal is to synthesize a control sequence for the leader such that, for any rational followers producing a best response, the leader’s STL task is guaranteed to be satisfied. We present an effective solution to this problem by transforming it into a single-stage optimization problem and leveraging counterexample guided synthesis techniques. We demonstrate that the proposed approach is sound and identify conditions under which it is optimal. Simulation results are also provided to illustrate the effectiveness of the proposed framework. © 2025, CC BY.

关键词： Control system synthesis

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Reduced-Order Consensus Protocols with Pure Relative Output: Design, Existence, and Dual Results

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IEEE Transactions on Automatic Control 2025年

作者： Ma, Yuwen Li, Xianwei Li, Shaoyuan Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai200240 China State Key Laboratory of Submarine Geoscience China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

This paper studies reduced-order dynamic consensus protocols for homogeneous linear multi-agent systems using pure relative output information. By applying H∞ control theory, a separation principle-like method with an additional small-gain constraint is proposed for designing an (nx-nu)th order protocol, where nx and nu are the numbers of states and inputs of each agent, respectively. Existence conditions for the protocol are then systematically discussed from the graph, low gain and high gain perspectives, respectively. It is revealed that the protocol always exists and can be found by the proposed design method for three cases: 1) directed acyclic networks, 2) agents with all eigenvalues in the closed left-half complex plane and 3) agents of minimum phase. Another reduced-order protocol of (nx - ny)th order is further discussed through duality. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Robust control

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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Combinations of weak classifiers

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第1期8卷 32-42页

作者： Ji, CY Ma, S Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute

To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity.

关键词： weak classifiers combinations of classifiers supervised learning generalization error space-complexity time-complexity

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Integrative Multi-Source Mapping of Coastal Environments Using Hy-perspectral Data Fusion Platforms

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Han, Huimin Por, Lip Yee Talpur, Bandeh Ali Hashim, Muhammad Yang, Jing Saidani, Taoufik Djumaniyazova, Mukhayya Xusinovna Madrakhimovich, Khudoynazarov Egambergan Talpur, Kazim Raza Hainan Vocational University of Science and Technology Mechanical and Electrical Engineering Col-lege Haikou571126 China Uni-versiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Trinity Col-lege Dublin School of Computer Science and Statistics Ireland National Textile Uni-versity Faisalabad Faisalabad Business School Pakistan Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Northern Border University Center for Scientific Research and Entrepreneurship Arar73213 Saudi Arabia Urganch State University Pedagogy and Psychology Department Uzbekistan Mamun University Uzbekistan Jamshoro Institute of Health Management and Re-search Sciences Sindh Pakistan

Hyperspectral images (HSIs) not only have a large number of spectral features, but also show a comprehensive spa-tial distribution of land cover and offer substantial advantages in the fine classification of ground materials. However, due to the high dimensionality and redundancy of HSIs and class imbalance in hyperspectral datasets, improving classification performance is still a huge challenge. Recently, graph-based learning algorithms are proven to be better classifiers for high dimensional data. Learning graph data and modeling spatial topological links be-tween features are significantly more accessible and more effective with graph-based deep learning, which has been demonstrated to be a promising approach. In the past few years, convolutional neu-ral networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs strug-gle to achieve good accuracy in low samples, while GCNs have a substantial computational cost. To resolve these issues, this paper proposes a feature fusion model using a 3D-CNN and hybrid-GAT named FFCHG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in im-proving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classifica-tion time with accuracy of more than 90% in all datasets and kappa with more than 91% for each dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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Software reliability analysis considering correlated component failures with coupling measurement framework

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Journal of systems engineering and Electronics 2015年第5期26卷 1114-1126页

作者： Xiaodan Li Yongfeng Yin Lance Fiondella Yibin Zhou School of Reliability and System Engineering Beihang University Department of Electrical & Computer Engineering University of Massachusetts

With progression of the digital age, the complexity of software continues to grow. As a result, methods to quantitatively assess characteristics of software have attracted significant attention. These efforts have led to a large number of new measures such as coupling metrics, many of which seek to consider the impact of correlations between components and failures on application reliability. However, most of these approaches set the coupling parameters arbitrarily by making assumptions instead of utilizing experimental data and therefore may not accurately capture actual coupling between components of software application. Since the coupling matrix is often set arbitrarily, the existing approaches to assess software reliability considering component correlation fail to reflect the real degree of interaction and relationships among software components. This paper presents an efficient approach to assess the software reliability considering correlated component failures, incorporating software architecture while considering actual internal coupling of software with an efficient approach based on multivariate Bernoulli(MVB) distribution. The unified framework for software coupling measurement is informed by a comprehensive survey of frameworks for objectoriented and procedure-oriented software. This framework enables the extraction of more accurate coupling among components. The effectiveness of this method is illustrated through an experimental study by applying it to a real-time software application.

关键词： correlated failures software coupling software architecture software reliability

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Methodology, progress and challenges of geoscience knowledge graph in International Big Science Program of Deep-Time Digital Earth

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Journal of Geographical Sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCAS Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application University of Chinese Academy of Sciences School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Department of Automation Tsinghua University State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing University State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Command Center of Natural Resources Comprehensive Survey School of Computer Science China University of Geosciences

Deep-time Earth research plays a pivotal role in deciphering the rates, patterns, and mechanisms of Earth's evolutionary processes throughout geological history, providing essential scientific foundations for climate prediction, natural resource exploration, and sustainable planetary stewardship. To advance Deep-time Earth research in the era of big data and artificial intelligence, the International Union of Geological Sciences initiated the “Deeptime Digital Earth International Big Science Program”(DDE) in 2019. At the core of this ambitious program lies the development of geoscience knowledge graphs, serving as a transformative knowledge infrastructure that enables the integration, sharing, mining, and analysis of heterogeneous geoscience big data. The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1) establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2) developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3) implementing a comprehensive three-tiered architecture encompassing basic, discipline-specific, and application-oriented knowledge graphs, spanning approximately 20 geoscience disciplines. Through its open knowledge framework and international collaborative network, this initiative has fostered multinational research collaborations, establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable, Accessible, Interoperable, Reusable) data practices in deep-time Earth systems research.

关键词： Deep-time Digital Earth International Big Science Program deep-time Earth geoscience knowledge graph

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The First Hardware Demonstration of a Universal Programmable RRAM-based Probabilistic computer for Molecular Docking

arXiv

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arXiv 2025年

作者： He, Yihan Hong, Ming-Chun Ding, Qiming Lin, Chih-Sheng Lai, Chih-Ming Fang, Chao Gong, Xiao Hou, Tuo-Hung Liang, Gengchiau Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Department of Electrical Engineering Institute of Electronics National Yang-Ming Chiao Tung University Hsinchu Taiwan Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Center on Frontiers of Computing Studies Peking University Beijing100871 China Industry Academia Innovation School National Yang-Ming Chiao Tung University Hsinchu Taiwan

Molecular docking is a critical computational strategy in drug design and discovery, but the complex diversity of biomolecular structures and flexible binding conformations create an enormous search space that challenges conventional computing methods. Although quantum computing holds promise for these challenges, it remains constrained by scalability, hardware limitations, and precision issues. Here, we report a prototype of a probabilistic computer (p-computer) that efficiently and accurately solves complex molecular docking for the first time, overcoming previously encountered challenges. At the core of the system is a p-computing chip based upon our artificial tunable probabilistic bits (p-bits), which are compatible with computing-in-memory schemes, based upon 180 nm CMOS technology and BEOL HfO2 RRAM. We successfully demonstrated the superior performance of the p-computer in practical ligand-protein docking scenarios. A 42-node molecular docking problem of lipoprotein with LolA-LolCDE complex—a key point in developing antibiotics against Gram-negative bacteria, was successfully solved. Our results align well with the Protein-Ligand Interaction Profiler tool. This work marks the first application of p-computing in molecular docking-based computational biology, which has great potential to overcome the limitations in success rate and efficiency of current technologies in addressing complex bioinformatics problems. © 2025, CC BY.

关键词： Molecular docking

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