Article
Computer Science, Artificial Intelligence
Yiying Zhang
Summary: The study introduces a new technique called specular reflection learning to improve the optimization performance of metaheuristic methods, particularly in enhancing the backtracking search algorithm. The effectiveness of specular reflection learning is demonstrated through experiments with various test functions and engineering design problems, showing its superiority over opposition-based learning.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Optics
Xianzhong Jian, Yizhuang Zhu
Summary: An accurate and robust parameter identification method is crucial for the optimization of photovoltaic systems. The newly proposed metaheuristic algorithm, MRao-1, enhances the global search ability without increasing time complexity, making it a promising alternative for parameter identification in PV models.
Article
Engineering, Electrical & Electronic
Chiwen Qu, Zenghui Lu, Fanjing Lu
Summary: This study proposes a novel metaheuristic algorithm called LSA for parameter estimation in solar PV models. Experimental results demonstrate that LSA outperforms other algorithms in terms of accuracy, convergence rate, and stability, while also showing competitive performance in solving real-world optimization problems with constraints.
JOURNAL OF COMPUTATIONAL ELECTRONICS
(2023)
Article
Chemistry, Analytical
Mingjing Wang, Long Chen, Huiling Chen
Summary: The research introduces a new algorithm called MLCPA for optimizing photovoltaic parameters, which utilizes opposition-based learning and level-based learning to improve the optimization performance. The effectiveness of MLCPA is demonstrated through comparisons with other algorithms and applications in parameter estimation for different types of photovoltaic modules.
Article
Nanoscience & Nanotechnology
Guohu Wang, Yong Zhao, Yongliang Yuan
Summary: An elite opposition-based learning (EOBL) strategy is proposed for the heat transfer search algorithm (HTSA) to achieve global optimization solutions for non-linear optimization problems. Experimental results show that the improved HTSA with EOBL strategy achieves the first rank in overall performance among the algorithms. By applying the IHTSA to determine the parameters of photovoltaic models, it has been shown to be an effective optimization algorithm.
Article
Thermodynamics
Xiang Chen, Kun Ding, Jingwei Zhang, Zenan Yang, Yongjie Liu, Hang Yang
Summary: Parameter identification of the PV model is crucial in PV research. A two-stage method based on MPM and IFDA is proposed for model parameter identification. The method preprocesses the measured I-V data, extracts rough parameters using MPM, and then performs precise identification using IFDA. Experimental results show that IFDA achieves the highest accuracy with an RMSE of 0.0024 A and exhibits good stability.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Energy & Fuels
Junfeng Zhou, Yanhui Zhang, Yubo Zhang, Wen-Long Shang, Zhile Yang, Wei Feng
Summary: The paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOLADE) to extract optimal parameters for PV cell models. The proposed algorithm shows improved accuracy, reliability, and computational efficiency in solving the problem.
Article
Automation & Control Systems
Fuqing Zhao, Jinlong Zhao, Ling Wang, Jie Cao, Jianxin Tang
Summary: This paper proposes a hierarchical knowledge-based multi-population cooperative evolution strategy guided backtracking search optimization algorithm (HKBSA) to improve the performance of the BSA. By utilizing domain knowledge and a multi-strategy mutation mechanism, HKBSA achieves better convergence speed, solution accuracy, and stability compared to other BSA variants.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Zhongbo Hu, Ting Zhou, Qinghua Su, Mianfang Liu
Summary: This paper proposes a niching backtracking search algorithm with adaptive local search to address the problem of multimodal multiobjective optimization. By adopting the affinity propagation clustering method to form multiple niches and developing a novel mutation based on affinity propagation clustering, the algorithm shows improved performance. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in various benchmark functions and practical application problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Thermodynamics
Wen Long, Tiebin Wu, Ming Xu, Mingzhu Tang, Shaohong Cai
Summary: The paper proposes a variant of butterfly optimization algorithm (EABOA) to identify unknown parameters of PV models, which shows better performance than other selected algorithms in terms of accuracy and reliability.
Article
Green & Sustainable Science & Technology
Wenjing Lei, Qing He, Liu Yang, Hongzan Jiao
Summary: This paper proposes an improved honey badger algorithm (IHBA) for accurately identifying the parameters of solar photovoltaic cells. The IHBA utilizes a spiral exploration mechanism and a density update factor to enhance the algorithm's global exploration ability and convergence speed, and employs a pinhole imaging strategy to improve optimization accuracy. Experimental results demonstrate that the IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness.
Article
Computer Science, Artificial Intelligence
Zhong-Qiang Wu, Chong-Yang Liu, De-Long Zhao, Yun-Qing Wang
Summary: This paper proposes a parameter identification method for the photovoltaic cell model based on the improved elephant herding optimization algorithm to overcome the shortcomings of traditional parameter identification methods. The use of the fast-moving operator significantly improves the convergence speed and global searching ability of the algorithm. The introduction of the elitist strategy replaces the worst individual with the optimal individual, improving the convergence speed and optimization time. Experimental results show that the improved algorithm outperforms other algorithms in the parameter identification of the photovoltaic cell model.
Article
Energy & Fuels
Wenguan Luo, Xiaobing Yu
Summary: In this study, a Quasi-Reflection based Multi-Strategy Cuckoo Search algorithm (QRMSCS) is proposed for accurate parameter estimation in different PV systems. The results of extensive experiments show that QRMSCS is the most robust and superior algorithm, indicating its promising potential for practical applications.
Article
Computer Science, Artificial Intelligence
Amr A. Abd El-Mageed, Amr A. Abohany, Hatem M. H. Saad, Karam M. Sallam
Summary: Given the complex properties of the photovoltaic model and the possibility of algorithms being stuck in local optima, extracting its parameters is a challenging task. However, accurately estimating these parameters is crucial for the performance of the PV system. This paper presents an improved queue search optimization algorithm called IQSODE, which utilizes the differential evolution technique and bound-constraint amendment procedure to efficiently extract parameter values for various PV models.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yuhan Wu, Xiyu Meng, Junru Zhang, Yang He, Joseph A. Romo, Yabo Dong, Dongming Lu
Summary: This study proposes a novel hybrid model that optimizes weights and thresholds to improve interpretability and accuracy. The hybrid model outperforms other models in multiple experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Heshan Wang, Yiping Zhang, Jing Liang, Lili Liu
Summary: In this study, a novel deep autoregression feature augmented bidirectional LSTM network (DAFA-BiLSTM) is proposed for time series prediction. It effectively extracts the transient characteristics of long interval sequential datasets and demonstrates good adaptive performance and robustness even in noisy environments through comparative experiments and statistical analysis.
Article
Robotics
Jing Liang, Hao Guo, Ke Chen, Kunjie Yu, Caitong Yue, Xia Li
Summary: With the rapid development of the national economy, the demand for electricity is increasing. The combustion of coal has led to serious environmental pollution, making it significant to improve energy conversion efficiency and reduce pollutant emissions. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed for boiler combustion modeling. Multi-objective optimization is also performed on the established model.
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Jing Liang, Kunjie Yu, Minghui Wang, Boyang Qu, Caitong Yue, Yinan Guo
Summary: This paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm to better solve constrained multi-objective optimization problems (CMOPs). DBEMTO evolves two populations to solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP) respectively and uses three evolutionary strategies for offspring generation. DBEMTO has performed more competitively compared to other state-of-the-art CMOEAs according to the final results.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Optics
Gaofeng Wu, Jing Liang, Fei Wang, Yangjian Cai
Summary: We propose an efficient method to tailor the spatial profile and degree of coherence of partially coherent light by manipulating its statistical properties in the spatial frequency domain. The relationship between beam profile and degree of coherence is analyzed, and experimental validation is provided. This approach allows for generating partially coherent light sources with controlled spatial coherence states.
Article
Automation & Control Systems
Jing Liang, Kangjia Qiao, Kunjie Yu, Boyang Qu, Caitong Yue, Weifeng Guo, Ling Wang
Summary: This article explores and utilizes the relationship between constrained Pareto front (CPF) and unconstrained Pareto front (UPF) to solve constrained multiobjective optimization problems (CMOPs). A new constrained multiobjective evolutionary algorithm (CMOEA) is presented by dividing the evolutionary process into learning stage and evolving stage. Experimental results show that the proposed method has better performance compared to state-of-the-art CMOEAs, indicating the promising use of the relationship between CPF and UPF in solving CMOPs.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Energy & Fuels
Guixian Chen, Jikai Si, Rui Nie, Jing Liang, Yihua Hu
Summary: This paper derives the analytical model of armature magnetic field (AMF) for slotless permanent magnet machines with arbitrary-phase equidirectional toroidal winding (EDTW), and analyzes the harmonic characteristics of the AMF. The results show that the analytical method is accurate and effective, providing a reference for further research and application of EDTW.
Article
Cell Biology
Ruiyao Hu, Jing Liang, Lan Ding, Wan Zhang, Yuying Wang, Yige Zhang, Ding Zhang, Lulu Pei, Xinjing Liu, Zongping Xia, Yuming Xu, Bo Song
Summary: Acute ischemic stroke (AIS) leads to elevated levels of neutrophils, which negatively affects patient survival. The study discovered that gasdermin D (GSDMD)-induced pyroptosis plays a crucial role in the pathophysiology of AIS. Knockout of GSDMD in mice demonstrated reductions in infarct size, improved neurological function, and increased survival rates after AIS. Additionally, pharmacological suppression of GSDMD showed positive effects on reducing pathological abnormalities and infarct volume, as well as improving neurological function. These findings provide new insights into the immunological modulation of neutrophils after AIS and suggest GSDMD suppression as a potential treatment strategy for stroke.
CELL DEATH DISCOVERY
(2023)
Article
Computer Science, Artificial Intelligence
Li Yan, Zhipeng Zhang, Jing Liang, Boyang Qu, Kunjie Yu, Kongyuan Wang
Summary: This paper proposes an adaptive segmented multi-objective evolutionary network architecture search (ASMEvoNAS) method, which efficiently searches for network architectures through adaptive segmented evaluation strategy, preference-based pre-selection strategy, and novel gene reservation-based crossover and directed connection-based mutation. Experimental results demonstrate that ASMEvoNAS achieves promising performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Jing Liang, Hongyu Lin, Caitong Yue, Kunjie Yu, Ying Guo, Kangjia Qiao
Summary: This article proposes a novel differential evolution algorithm for solving constrained multimodal multiobjective optimization problems (CMMOPs). The algorithm uses the speciation mechanism to obtain more feasible Pareto-optimal solutions and adopts an improved environment selection criterion to enhance diversity. It can not only obtain feasible solutions but also retain more well-distributed feasible Pareto-optimal solutions.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Kunjie Yu, Boyang Qu, Jing Liang, Caitong Yue, Xuanxuan Ban
Summary: In this article, an evolution-based constrained multiobjective feature extraction method (ECMOFE) is proposed, which leverages the information generated in the evolutionary process to form the feature matrix. Two populations are created to optimize constraints and objectives, and two complementary evolutionary operators are used to generate offspring for each population. The successful rate of offspring individuals generated by each operator of each population is recorded to form the feature matrix. Based on the formed features, several algorithm recommendation methods are built on the basis of classifiers. The results based on multiple metrics show the effectiveness of the proposed ECMOFE.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Yaxin Li, Jing Liang, Caitong Yue, Kunjie Yu, Hao Guo
Summary: This study proposes an incremental random walk (IRW) algorithm for sampling the fitness landscape of continuous optimization problems. IRW achieves excellent performance in terms of distribution of sampling points and coverage by incorporating an incremental perturbation mechanism and a mirrored boundary handling method. Experimental results demonstrate the superiority and reliability of IRW across various problem dimensions.
Article
Computer Science, Artificial Intelligence
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: Modern data collection technologies often generate numerous features in a dataset, making it challenging to identify relevant features. This study proposes a feature clustering-assisted feature selection method to overcome this issue. The method utilizes correlation measures to group features and embeds this knowledge into the encoding method and search process. It also employs a niching-based mutation operator to explore different feature subsets with similar classification performance. Moreover, a modification operator is introduced to increase population diversity and improve feature selection performance. Experimental results demonstrate that the proposed method outperforms other popular feature selection methods in terms of classification accuracy and feature subset size.
PATTERN RECOGNITION
(2023)
Article
Automation & Control Systems
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: Feature selection can reduce problem dimensionality while maintaining or increasing data discrimination by selecting a small subset of relevant features. However, many existing approaches overlook the existence of multiple optimal solutions to feature selection problems. The proposed method in this study employs a niching-based differential evolution with duplication analysis to search for multiple optimal feature subsets, achieving higher classification accuracy compared to other methods and discovering different feature subsets with similar or the same accuracy.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yanqi Wei, Jikai Si, Rui Nie, Peixin Wang, Shuai Xu, Chun Gan, Jing Liang
Summary: This article proposes a unified analytical model for the armature magnetic field of slotless permanent magnet motors with equidirectional toroidal winding. The model can clarify the operating mechanism of the armature magnetic field, analyze its characteristics quantitatively, and distinguish it from other windings. It has been verified through experiments.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Wu, Xun Su, Jing Liang, Zhongchuan Sun, Lihong Zhong, Yangdong Ye
Summary: In this paper, we propose a new solution for sequential recommendation called GMRec. We improve the accuracy and effectiveness of recommendation by using a graph gating-mixer recommender module. Extensive experiments show that GMRec outperforms recent state-of-the-art methods on multiple datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
