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Multi-stage Global and Local Cooperative Constrained Multi-objective Evolutionary Algorithm 6

Multi-stage Global and Local Cooperative Constrained Multi-o...

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6th international conference on data-driven optimization of complex systems (docs)

作者： Wang, Shunge Qiu, Yifeng Li, Wenji Wang, Zhaojun Xu, Biao Zhuang, Jiafan Hao, Zhifeng Fan, Zhun Shantou Univ Shantou Guangdong Peoples R China UESTC Shenzhen Inst Adv Study Shenzhen Peoples R China

ISBN: (纸本)9798350377859;9798350377842

When solving constrained multi-objective optimization problems (CMOPs), the primary challenge is to quickly search for the true Pareto fronts (PFs) while ensuring diversity. However, some constrained multi-objective evolutionary algorithms (CMOEAs) employ a single search strategy, making it difficult for their populations to maintain convergence, diversity, and feasibility simultaneously. To address this issue, we propose a novel multi-stage global and local collaborative CMOEA named MSGLC, which includes two different search stages. In the first stage, constraints are ignored, and a CMOP is decomposed into multiple single-objective sub-problems without considering any constraints, allowing the population to quickly search the boundaries of unconstrained PFs, thus improving convergence. In the second stage, through weak co-evolution of the main and auxiliary populations, the main population considers the original CMOP, while the auxiliary population considers the e-relaxed CMOP. they respectively adopt local and global differential evolution operators for offspring generation. this can help the populations effectively get across multiple large infeasible areas and quickly search for the constrained PF. Furthermore, we introduce a computational resource allocation strategy based on sub-regions, concentrating computational resources in potentially search areas, thereby enhancing the search efficiency of the proposed algorithm. Comparative experimental results demonstrate that the proposed MSGLC significantly outperforms five other state-of-the-art CMOEAs on the LIRCMOP benchmark problems.

关键词： Constrained multi-objective optimization Multi-stage search Global and local cooperation

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A Multi-objective optimization Algorithm and Process for Modelica Model 4

A Multi-objective Optimization Algorithm and Process for Mod...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Zhang, Congcong Xu, Cheng Sun, Erxin Zang, Dandan Automotive Data of China Co. Ltd Department of Nev Reseach Tianjin China Automotive Data of China Co. Ltd Department of Software Development Tianjin China

ISBN: (纸本)9781665463997

With the increasing complexity of modern mechanical and electrical products, the single objective optimization function is no longer competent for the overall optimization task of modern complex engineering systems. However, in the current commercial software based on Modelica model, there are few functions for multi-objective optimization, and the current multi-objective optimization algorithm has the problems of insufficient approximation of the optimal solution set and uneven distribution of the solution set. In order to solve this problem, Combined with the characteristics of Modelica model and NSGA-Ċ algorithm, this paper proposes a multi-objective optimization algorithm and process for Modelica model. this scheme provides a multi-objective optimization design process for the current modeling and simulation platform, and also avoids some defects of the existing multi-objective optimization technology. Hope to provide new ideas and solutions for the overall optimization and improvement of modern mechanical and electrical products. © 2022 IEEE.

关键词： Multiobjective optimization

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Novel data-driven Approach for Optimizing Virtual Inertia and Primary Frequency Response in Power systems 4

Novel Data-Driven Approach for Optimizing Virtual Inertia an...

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4th international conference on New Energy and Power Engineering, ICNEPE 2024

作者： Liu, Qiang Xu, Xiao Cui, Jia Zhao, Peng Wang, Gang Wang, Runxuan State Grid Jinzhou Electric Power Supply Company Liaoning Jinzhou121000 China Shenyang University of Technology School of Electrical Engineering Liaoning Shenyang China

ISBN: (纸本)9798331516086

the continuous integration of renewable energy sources into the power grid poses significant challenges to the stability of the power system. this paper investigates the optimization of virtual inertia parameters in wind power systems to mitigate these challenges. Firstly, the impact of the virtual inertia parameters on the system frequency constraints and small-signal stability, which are crucial for maintaining grid stability, is analyzed. Secondly, an optimization model is formulated to enhance the frequency response and small disturbance stability considering the operational constraints of the wind turbine. thirdly, in order to simplify the complex stability constraints, a data-driven approach using support vector machine (SVM) is introduced to convert the implicit constraints into explicit ones. Finally, the proposed method is evaluated through simulations, which show that the method can significantly improve the frequency regulation and stability margin of the system, highlighting the potential of optimizing virtual inertia parameters in ensuring the reliable operation of modern power systems with high renewable energy penetration. © 2024 IEEE.

关键词： Windmill

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A data-driven Prediction Method of Short-Term Cooling Load Based on Machine Learning and Regression Analysis 4

A Data-Driven Prediction Method of Short-Term Cooling Load B...

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4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Guo, Jie Zhao, Qiang University of Jinan School of Physics and Technology Jinan China

ISBN: (纸本)9798350366914

the modeling and prediction of air conditioning load in residential buildings has been an indispensable step in energy conservation and cost reduction. this paper proposes a data-driven load prediction model based on machine learning technology. Firstly, the input data is processed by feature engineering according to the Pearson Correlation Coefficient (PCC) between load and features. Secondly, three machine learning algorithms (SVR, MLP, and RF) and Pearson Correlation Coefficient (GTO) optimization algorithm are used for prediction. Finally, the predicted load is output by linear analysis and weighted regression. the model is verified by using residential building simulation data generated by Dest software. the influence of model integration on the overall prediction results and the error distribution of predicted load in different periods are analyzed and discussed. the Mean Absolute Error (EMAE) of predicted load is 1.52kW, and the Mean Absolute Percentage Error (EMAPE) is 10.7%. © 2024 IEEE.

关键词： Prediction models

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LSBO-NAS: Latent Space Bayesian optimization for Neural Architecture Search 4

LSBO-NAS: Latent Space Bayesian Optimization for Neural Arch...

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4th international conference on Control and Robotics (ICCR)

作者： Rao, Xuan Xiao, Songyi Li, Jiaxin Wu, Qiuye Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665486415

From the perspective of data stream, neural architecture search (NAS) can be formulated as a graph optimization problem. However, many state-of-the-art black-box optimization algorithms, such as Bayesian optimization and simulated annealing, operate in continuous space primarily, which does not match the NAS optimization due to the discreteness of graph structures. To tackle this problem, the latent space Bayesian optimization NAS (LSBO-NAS) algorithm is developed in this paper. In LSBO-NAS, the neural architectures are represented as sequences, and a variational auto-encoder (VAE) is trained to convert the discrete search space of NAS into a continuous latent space by learning the continuous representation of neural architectures. Hereafter, a Bayesian optimization (BO) algorithm, i.e., the tree-structure parzen estimator (TPE) algorithm, is developed to obtain admirable neural architectures. the optimization loop of LSBO-NAS consists of two stages. In the first stage, the BO algorithm generates a preferable architecture representation according to its search strategy. In the second stage, the decoder of VAE decodes the representation into a discrete neural architecture, whose performance evaluation is regarded as the feedback signal for the BO algorithm. the effectiveness of the developed LSBO-NAS is demonstrated on the NAS-Bench-301 benchmark, where the LSBO-NAS achieves a better performance than several NAS baselines.

关键词： Neural architecture search deep generative model variational auto-encoder Bayesian optimization unsupervised representation learning

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2022 4th international conference on System Reliability and Safety Engineering, SRSE 2022

2022 4th International Conference on System Reliability and ...

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4th international conference on System Reliability and Safety Engineering, SRSE 2022

ISBN: (纸本)9781665473880

the proceedings contain 93 papers. the topics discussed include: research on the classification method of reliability critical parts and important parts;condition-based maintenance for two-component series systems considering component reallocation;resilience modeling of interdependent supply chain networks with company collaboration against ripple effects;research on the construction method of system off-nominal states model based on ontology model;an improved approach based on linear programming model for assessing rail transit system resilience under disruptions;put theory into practice knowledge graph based aviation quality reliability knowledge system;uncertainty analysis technology of complex system based on evidence theory;leveraging ground test data for aero-engine thrust estimation;reliability assessment of man-machine systems subject to probabilistic common cause errors;application of software reliability in civil aircraft airborne software development;reliability modeling and analysis for space phased-mission system;on modeling of repairable systems with multi-output Gaussian convolution process;and reliability optimization of the filling and discharging system for lead-based fast reactor.

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AI-driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP 4

AI-Driven Health Monitoring of Distributed Computing Archite...

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4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Sun, Xiaoxuan Yao, Yue Wang, Xiaoye Li, Pochun Li, Xuan Mountain View United States Northeastern University PortlandME United States Western University LondonON Canada Northeastern University BostonMA United States Columbia University New YorkNY United States

ISBN: (纸本)9798331528973

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack of accuracy and interpretability of traditional methods in node health status judgment, this paper proposes a health status judgment method based on XGBoost and combines the SHAP method to analyze the interpretability of the model. through experiments, it is verified that XGBoost has superior performance in processing complex features and nonlinear data of edge computing nodes, especially in capturing the impact of key features (such as response time and power consumption) on node status. SHAP value analysis further reveals the global and local importance of features, so that the model not only has high-precision discrimination ability but also can provide intuitive explanations, providing data support for system optimization. Research shows that the combination of AI technology and computer system optimization can not only realize the intelligent monitoring of the health status of edge computing nodes but also provide a scientific basis for dynamic optimization scheduling, resource management and anomaly detection. In the future, with the in-depth development of AI technology, model dynamics, cross-node collaborative optimization and multimodal data fusion will become the focus of research, providing important support for the intelligent evolution of edge computing systems. © 2024 IEEE.

关键词： Electronic health record

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Architecture optimization and Core Code De-Redundancy of Educational Management Information System in Higher Vocational Colleges Under the Background of data Governance Algorithm 4

Architecture Optimization and Core Code De-Redundancy of Edu...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Mojun, Xiang Tingmin, Liu Chengdu Agricultural College Sichuan Chengdu611130 China

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

From the two aspects of data standardization and data cleaning, formulate data abnormality judgment rules, establish data abnormality processing methods, and conduct data governance on online monitoring data. Summarized and put forward the concepts and methods suitable for the reengineering of teaching management process in higher vocational colleges, and after the practical test of teaching management, integrated and optimized some core teaching management processes in the information environment, as well as the optimization of its structure and the redundancies of core codes. Briefly analyze. By clarifying the allocation of powers and responsibilities, management systems, governance objectives, and governance routes, etc., we will improve my country's government data governance system and provide effective organizational guarantees for government data governance. © 2022 IEEE.

关键词： Redundancy

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Proceedings - international conference Advancement in data Science, E-Learning and Information systems, ICADEIS 2022

Proceedings - International Conference Advancement in Data S...

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4th international conference Advancement in data Science, E-Learning and Information systems, ICADEIS 2022

ISBN: (纸本)9781665463874

the proceedings contain 38 papers. the topics discussed include: developing IT strategic transformation of smart village concept for Indonesian village model;social media analysis on aquaculture supplychain management: a case study on freshwater lobsters;selection of the best color space for image steganography with the least significant bit method;defense-in-depth security strategy in Log4j vulnerability analysis;exploration of ECG-based real-time arrhythmia detection: a systematic literature review;prediction model of mortality with respiratory rate, oxygen saturation and heart rate using logistic regression;application of reverse engineering and simulation in the design of a pontoon drum for coconut shell combustion;optimization of Hijaiyah letter handwriting recognition model based on deep learning;and event-driven architecture to improve performance and scalability in microservices-based systems.

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Fuzzy model predictive control for Alternating Current excitation generators

Fuzzy model predictive control for Alternating Current excit...

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4th international Power Electronics and Motion Control conference (IPEMC 2004)

作者： Fan, SS Wang, YN Changsha Univ Sci & Technol Dept Elect & Informat Engn Changsha Hunan Peoples R China

ISBN: (纸本)7560518699

Alternating Current excitation generators (ACEG) can adjust the active power and inactive power flexibly and improve the stability of power system. the key to enhance the power system's stability is to choose appropriate ACEG's excitation control method. Conventional excitation controllers are unable to perform optimally over the full range of operation conditions and disturbances, due to the highly complex, non-linear nature of power systems. In this paper, Fuzzy model predictive control is proposed to cope with the problem. T-S fuzzy model is employed to appropriate the non-linear object. the fuzzy model is derived from input-output data by means of product-space fuzzy clustering, similarity driven rule base simplification is applied to detect and merges compatible fuzzy sets in the model and a new validity measure is adopted to determine the number of clusters. All these techniques make the fuzzy model transparent and accurate. the critical element in fuzzy model predictive control is the nonconvex optimization problem, iterative optimization techniques are mostly slow due to computational complexity, this hamper its application to fast system. In order to solve the problem, Branch-and-bound optimization method is adopted. the fuzzy model predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Simulation test under large disturbance at various operating points is made. the results show the fuzzy model based predictive controller is effective and feasible. It performs well over a wide range of system disturbance and improves dynamic characteristic of ACEG system.

关键词： fuzzy model predictive control T-S model optimization ACEG excitation control

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