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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time. © 2024 IEEE.

关键词： Optimization algorithms

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic Engineering and Renewable Energy systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Discriminative Word Alignment Over Multiple Word Segmentations

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Chinese Journal of Electronics 2025年第2期23卷 263-270页

作者： Ning Xi Xinyu Dai Shujian Huang Jiajun Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Conventional bilingual word alignment is conducted on sentence pairs with single word segmentation for languages such as Chinese, viz. Single-segmentation-based word alignment (SSWA). However, SSWA may run the risk of losing optimal word segmentation granularities or causing data sparseness in word alignment. This paper proposes Multiple-segmentation-based word alignment (MSWA). In MSWA, diverse and complementary knowledge in multiple word segmentations can be employed to lower the above risks in word alignment. Given $k$ word segmentations of a Chinese sentence, a skeleton segmentation is firstly constructed. The alignment between the skele-ton segmentation and the parallel English sentence is log-linearly modeled, where various features defined over multiple word segmentations are incorporated. The Viterbi alignment, the alignment with the highest score, is mapped back to $k$ word alignments based on $k$ segmentations respectively. Experimentally, MSWA outperformed SSWA on all $k$ segmentations in both alignment quality and translation performance.

关键词： Translation Viterbi algorithm Social sciences Education Medical services Skeleton

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Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

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Chinese Journal of Electronics 2023年第1期32卷 64-75页

作者： TANG Minli XIE Shaomin LIU Xiangrong Department of Computer Science and Technology School of Informatics Xiamen University School of Big Data Engineering KaiLi University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

关键词： Training Speech recognition Artificial neural networks Feature extraction Stability analysis Character recognition Machine translation

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ChatGPT: potential, prospects, and limitations

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Frontiers of Information technology & Electronic Engineering 2024年第1期25卷 6-11页

作者： Jie ZHOU Pei KE Xipeng QIU Minlie HUANG Junping ZHANG School of Computer Science Fudan UniversityShanghai 200433China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433China

Recently,OpenAI released Chat Generative Pre-trained Transformer(ChatGPT)(Schulman et al.,2022)(https://***),which has attracted considerable attention from the industry and academia because of its impressive *** is the first time that such a variety of open tasks can be well solved within one large language *** better understand ChatGPT,we briefly introduce its history,discuss its advantages and disadvantages,and point out several potential ***,we analyze its impact on the development of trustworthy artificial intelligence,conversational search engine,and artificial general intelligence.

关键词： artificial limitations intelligence

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Comprehensive Learning Strategy Enhanced Chaotic Whale Optimization for High-dimensional Feature Selection

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Journal of Bionic Engineering 2023年第6期20卷 2973-3007页

作者： Hanjie Ma Lei Xiao Zhongyi Hu Ali Asghar Heidari Myriam Hadjouni Hela Elmannai Huiling Chen Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China Department of Computer Sciences College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.Box 8442811671 RiyadhSaudi Arabia Department of Information Technology College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityP.O.Box 8442811671 RiyadhSaudi Arabia

Feature selection(FS)is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics,finance,and *** FS approaches,however,frequently struggle to identify the most important characteristics when dealing with high-dimensional *** alleviate the imbalance of explore search ability and exploit search ability of the Whale Optimization Algorithm(WOA),we propose an enhanced WOA,namely SCLWOA,that incorporates sine chaos and comprehensive learning(CL)*** them,the CL mechanism contributes to improving the ability to *** the same time,the sine chaos is used to enhance the exploitation capacity and help the optimizer to gain a better initial *** hybrid performance of SCLWOA was evaluated comprehensively on IEEE CEC2017 test functions,including its qualitative analysis and comparisons with other *** results demonstrate that SCLWOA is superior to other algorithms in accuracy and converges faster than ***,the variant of Binary SCLWOA(BSCLWOA)and other binary optimizers obtained by the mapping function was evaluated on 12 UCI data ***,BSCLWOA has proven very competitive in classification precision and feature reduction.

关键词： Feature selection Whale Optimization Algorithm Binary optimizer Global optimization

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A quantum federated learning framework for classical clients

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science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan Li Qiaoyan Wen Sujuan Qin Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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Multi-Scale Automated Self-Supervised Learning for Recommendation

Multi-Scale Automated Self-Supervised Learning for Recommend...

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2023 International Conference on intelligent Management and Software Engineering, IMSE 2023

作者： Yan, Yuhang Gao, Yujie Tian, Liqin School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China School of Computer North China Institute of Science and Technology Yanjiao China

ISBN: (纸本)9798350394146

Automated self-supervised learning frameworks have shown significant results in the recommendation domain. However, existing methods such as AutoCF use graph convolutional networks and graph attention mechanisms that are limited to a single expression granularity and cannot fully capture the multi-granularity of user interests and product attributes. To address this problem, we propose MS-AutoCF, a multi-scale automated self-supervised learning recommender system that obtains user and product representations at different granularities through multi-layer graph convolution to match the global and local details of their interest preferences. Meanwhile, we design a query key value-based multi-head graph attention module that can adaptively aggregate and enhance the representations with different granularities. Experimental results show that the MS-AutoCF system achieves significant improvements over existing single-scale methods on several public datasets. ©2023 IEEE.

关键词： Supervised learning

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Julolidine functionalized benzimidazoline-doped fullerene derivatives for efficient and stable perovskite solar cells

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Interdisciplinary Materials 2024年第3期3卷 369-379页

作者： Yanqing Zhu Chenglong Li JiaHui Chen Yuxi Zhang Jianfeng Lu Min Hu Wangnan Li Fuzhi Huang Yi-Bing Cheng Hyesung Park Shengqiang Xiao State Key Laboratory of Silicate Materials for Architectures Wuhan University of TechnologyWuhanChina State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhanChina School of Electronic and Electrical Engineering Hubei Province Engineering Research Centre for Intelligent Micro-Nano Medical Equipment and Key TechnologiesWuhan Textile UniversityWuhanChina Hubei Key Laboratory of Low Dimensional Optoelectronic Material and Devices Hubei University of Arts and ScienceXiangyangHubeiChina KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoulRepublic of Korea Department of Integrative Energy Engineering Korea UniversitySeoulRepublic of Korea Hubei Key Laboratory of Fuel Cell WuhanChina

Fullerene derivatives are highly attractive materials in solar cells,organic thermoelectrics,and other ***,the intrinsic low electron mobility and electrical conductivity restrict their potential device performance,such as perovskite solar cells(PSCs).Herein,we successfully enhanced the electric properties and morphology of phenyl-C61-butyric acid methyl ester(PCBM)by n-doping it with a benzimidazoline derivative,9-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)-julolidine(JLBI-H)via a solution *** found the n-doping can not only improve the conductivity and optimize the band alignment but also enable the PCBM to have a constantly strong charge extraction ability in a wide temperature from 173 to 373 K,which guarantees a stable photovoltaic performance of the corresponding PSCs under a wide range of operating *** the JLBI-H-doped PCBM,we improved the efficiency from 17.9%to 19.8%,along with enhanced stability of the nonencapsulated devices following the aging protocol of ISOS-D-1.

关键词： charge extraction electron mobility n-doping PSCs wide operation temperature

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