检索结果-内蒙古大学图书馆

An adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 177-217页

作者： Liu, Xiaotong Xu, Ying Wang, Tianlei Zeng, Zhiqiang Zhou, Zhiheng Zhai, Yikui Wuyi Univ Sch Mech & Automat Engn Jiangmen 529020 Peoples R China Jiangmen Key Lab Kejie Semicond Bonding Technol & Jiangmen 529020 Peoples R China Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China South China Univ Technol Coll Future Technol Guangzhou 510640 Peoples R China

Integrating multiple search operators to utilize their different characteristics in order to improve the performance of evolutionary algorithms is a challenging task. This paper proposes an adaptive combination algorithm that integrates four search operators, called RLACA. RLACA introduces a reinforcement learning-based adaptive search operator selection mechanism (RLAS) to dynamically choose the most suitable search operator based on the individual states. Additionally, a neighborhood search strategy based on differential evolution (NSDE) is incorporated to mitigate premature convergence by increasing population diversity. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite. The experimental results demonstrate that RLAS can adaptively select a suitable search operator and NSDE can enhance the algorithm's local search capability, thereby improving the performance of RLACA. Compared with the four basic algorithms and four combination algorithms, RLACA performs better in both convergence speed and resolution accuracy.

关键词： reinforcement learning combination algorithm evolutionary algorithm swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis

引用

Chinese Journal of Aeronautics 2024年第12期37卷 258-274页

作者： Xiukai YUAN Weiming ZHENG Yunfei SHU Yiwei DONG School of Aerospace Engineering Xiamen UniversityXiamen 361005China

Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters *** estimate the Imprecise Time-variant Failure Probability Function(ITFPF)and derive the imprecise reliability results as a byproduct,Adaptive combination Augmented Line Sampling(ACALS)is *** consists of three integrated features:Augmented Line Sampling(ALS),adaptive strategy,and the optimal *** is adopted as an efficient analysis tool to obtain the failure probability function *** ***,the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time ***,the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance(C.o.V.)of the ITFPF ***,the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal ***,the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering *** examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.

关键词： Time-variant reliability Imprecise reliability Line sampling Adaptive strategy combination algorithm

来源：评论

学校读者我要写书评

暂无评论

Global failure probability function estimation based on an adaptive strategy and combination algorithm

引用

RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年 231卷

作者： Yuan, Xiukai Qian, Yugeng Chen, Jingqiang Faes, Matthias G. R. Valdebenito, Marcos A. Beer, Michael Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China TU Dortmund Univ Chair Reliabil Engn Leonhard Euler Str 5 D-44227 Dortmund Germany Leibniz Univ Hannover Inst Risk & Reliabil Callinstr 34 Hannover Germany Univ Liverpool Inst Risk & Uncertainty Peach St Liverpool L69 7ZF England Tongji Univ Int Joint Res Ctr Resilient Infrastruct Shanghai 200092 Peoples R China Tongji Univ Int Joint Res Ctr Engn Reliabil & Stochast Mech Shanghai 200092 Peoples R China

The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables of a reliability problem. Knowledge on this FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. However, its calculation is usually a challenging task. Therefore, this paper presents an efficient approach for estimating the FPF based on an adaptive strategy and a combination algorithm. The proposed approach involves three basic elements: (1) a Weighted Importance Sampling approach, which allows determining local FPF estimates;(2) an adaptive strategy for determining at which realisations of the distribution parameters it is necessary to perform local FPF estimation;and (3) an optimal combination algorithm, which allows to aggregate local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach.

关键词： Failure probability function Importance sampling combination algorithm Adaptive strategy

来源：评论

学校读者我要写书评

暂无评论

A Novel combination algorithm of Different Methods of Maximum Power Point Tracking for Grid-Connected Photovoltaic Systems

引用

JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2021年第4期143卷 041003页

作者： Ali, Mahmoud N. Benha Univ Fac Engn Dept Elect Engn 108 Shoubra St Cairo Egypt

This paper proposes a novel algorithm to combine two or more techniques of maximum power point tracking (MPPT) to increase the output dc power and the efficiency of photovoltaic (PV) arrays. Different MPPT methods have dissimilar responses for the same environmental circumstances. The combination of multiple methods has the advantage of ever acquiring maximum power as the environmental conditions change. The proposed algorithm is used to enhance the selection of the most suitable duty cycle of the combined methods, based on the power-voltage characteristics, to get the best tracking response. Multiple classical and/or artificial intelligence (AI)-based MPPT methods can be combined based on this selection algorithm. To demonstrate its effectiveness, two examples are illustrated. The first one is the combination of two classical MPPT methods, which are the incremental conductance (INC) and the perturb-and-observe (P&O). The second example is to combine two AI-based MPPT, which are the artificial neural network (ANN) and the fuzzy logic control (FLC). The simulation results of the application of the proposed algorithm to a grid-connected PV system model justify its capability to acquire better static and dynamic responses than the responses of individual MPPT methods.

关键词： maximum power point tracking combination algorithm artificial neural network fuzzy logic incremental conductance perturb-and-observe grid-connected PV systems

来源：评论

学校读者我要写书评

暂无评论

Study on Seismic Magnitude Prediction Based on combination algorithm 9th

Study on Seismic Magnitude Prediction Based on Combination A...

引用

9th International Conference on Modelling, Identification and Control (ICMIC)

作者： Zhou, Wan-zhen Kan, Jing-sen Sun, Shuo Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050000 Hebei Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Shandong Peoples R China

ISBN: (纸本)9781509065738

Earthquake as a kind of natural disaster, it has a great destruction, but the magnitude of earthquake is closely related to the latitude, longitude and the depth of focal. It is very important to realize the magnitude prediction by data mining. In order to reduce the damage degree of earthquake to human's life and enhance the prediction ability of magnitude at different sites and depth of source, the support vector machine (SVM) and neural network algorithm are used to construct the magnitude prediction model based on existing seismic data, and the prediction of possible earthquake magnitude at different locations and focal depth is realized based on combination algorithm of support vector machine and neural network. Experiment results shows that the predictive ability of combination algorithm is better than using traditional SVM or neural network obviously, and have large extent overcome the disadvantage of support vector machine in solving multi-classification problems and bad selection of artificial neural network parameters easily lead to over-fitting or under-fitting and other disadvantages.

关键词： Data mining combination algorithm support vector machine neural network magnitude prediction

来源：评论

学校读者我要写书评

暂无评论

Study on Seismic Magnitude Prediction Based on combination algorithm

Study on Seismic Magnitude Prediction Based on Combination A...

引用

2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)

作者： Wan-zhen Zhou Jing-sen Kan Shuo Sun the School of Information Science & Engineering Hebei University of Science and Technology the School of Information Science & Engineering Shandong University

Earthquake as a kind of natural disaster,it has a great destruction,but the magnitude of earthquake is closely related to the latitude,longitude and the depth of *** is very important to realize the magnitude prediction by data *** order to reduce the damage degree of earthquake to human's life and enhance the prediction ability of magnitude at different sites and depth of source,the support vector machine(SVM) and neural network algorithm are used to construct the magnitude prediction model based on existing seismic data,and the prediction of possible earthquake magnitude at different locations and focal depth is realized based on combination algorithm of support vector machine and neural *** results shows that the predictive ability of combination algorithm is better than using traditional SVM or neural network obviously,and have large extent overcome the disadvantage of support vector machine in solving multi-classification problems and bad selection of artificial neural network parameters easily lead to over-fitting or under-fitting and other disadvantages.

关键词： Data mining combination algorithm support vector machine neural network magnitude prediction

来源：评论

学校读者我要写书评

暂无评论

A combination-based machine learning algorithm estimating impacts of social, economic, and environmental on resident health-on China?s provincial panel data

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期123卷

作者： Wen, Li Pan, Wei Liao, Shujie Pan, Wulin Xu, Hui Hu, Cheng Renmin Univ China Sch Appl Econ Beijing 100872 Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Canc Biol Res CtrDept Obstet & Gynecol Wuhan 430030 Hubei Peoples R China Wuhan Univ Sch Econ & Management Wuhan 430072 Peoples R China Wuhan Univ Dong Fureng Inst Econ & Social Dev Wuhan 430072 Peoples R China

The factors influencing residents health have become complex and intertwined with the development of economy and society. Traditional research with a single factor on health will not provide an accurate picture of the situation. This paper collects data on economic, environmental and social factors to estimate their impact on regional health. Considering the data is multi-source and complex, this paper proposes a combined feature importance algorithm, which weighted the feature importance of RF, XGB and SOIL. The algorithm does not depend on the data and adaptively approximates the true results. The results show that economic factors have a significant and direct impact on health, environmental factors have a lag correlation with health level, and social factors have a more complicated effect on health. Finally, we provide policy suggestions for health on economic, environmental, and social development.

关键词： Residents health combination algorithm Feature importance

来源：评论

学校读者我要写书评

暂无评论

Optimal operation of single unit in pumping station based on a combination of orthogonal experiment and 0-1 integer programming algorithm

引用

WATER SUPPLY 2022年第11期22卷 7905-7915页

作者： Wang, Shiwen Cheng, Jilin Zhu, Bowen Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225009 Peoples R China Yangzhou Univ Inst Modern Rural Water Conservancy Yangzhou 225009 Peoples R China

The frequent start-stop scheme in the operation management of large pumping stations causes a series of problems, e.g., energy consumption and potential safety hazard. Therefore, we established a mathematical model for the optimal operation of a large-scale pumping station considering the start-stop loss of machine. The objectives of the optimization model included electricity charges for pumping station operation and the start-stop loss of pumping station. The blade angle and start-stop scheme were taken into account as the double decision variables, while the target water extraction (equality constraint), start-stop scheme and power were the constraint conditions. A combination of orthogonal experiment and 0-1 integer programming algorithm was proposed to exploring the optimal solution of the model. A real case of single-unit pump station in China was applied to prove the effectiveness of the algorithm. The results showed that the optimization efficiency of proposed algorithm was 4%-10% higher than that of genetic algorithm. Additionally, the maximum optimization benefit was obtained when the number of start-stop was less than 3 times during operation process. The proposed algorithm provided a reference for similar complex nonlinear models.

关键词： blade angle combination algorithm pump station single unit start-stop scheme test selection

来源：评论

学校读者我要写书评

暂无评论

EMD-based multi-algorithm combination model of variable weights for oil well production forecast

引用

ENERGY REPORTS 2022年 8卷 13389-13398页

作者： Cao, Yu Liu, Shanke Cao, Xiaopeng Liu, Xinyi Hu, Huifang Zhang, Tingting Yu, Lijun Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Sinopec Shengli Oilfield Explorat & Dev Res Inst Dongying 257015 Shandong Peoples R China Shanghai Jiao Tong Univ Coll Smart Energy 665 Jianchuan Rd Shanghai 200240 Peoples R China

Oilfields in high or ultra-high water-cut period are of high nonlinearity and heterogeneity, thus complicated in its internal physics mechanism. Corresponding production data is often of small amount due to large timesteps. In addition, numerous mechanism factors that are usually a prerequisite for traditional data-driven prediction methods are to be determined with extra human effort on a well basis, deviating from the intention of oilfield digital transformation. In response to these obstacles, this research proposes an EMD (Empirical Mode Decomposition)-based variable-weight combination model algorithm (EMD-Combined model): It does not require inputs other than its own historical oil production time series, but rather automatically constructs and filters input features via EMD, time-lag reconstruction and Spearman correlation analysis instead. A dynamic Variable-Weight combination Methods of four machine learning models with different strengths is constructed to carry out prediction. Experimental results show that the EMD-Combined model is more accurate than the solely use of any of the four single models listed above, or models solely exerted with EMD without model-wise combination. In terms of the ease of usage, deployment and "stability"of prediction quality, EMD-Combined model offers a possibility of improvement in real-life practice. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Petroleum engineering Petroleum production forecast Machine learning combination algorithm Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Secure combination of Untrusted Time Information Based on Optimized Dempster-Shafer Theory

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Yang Luo, Yujie Fang, Lujun Zhang, Yichen Huang, Wei Sun, Ao Zhang, Shuai Zhang, Tao Zhou, Chuang Ma, Li Yang, Jie Wang, Heng Pan, Yan Shao, Yun Chen, Xing Chen, Ziyang Yu, Song Luo, Bin Guo, Hong Xu, Bingjie Inst Southwestern Commun Natl Key Lab Secur Commun Chengdu 610041 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Peking Univ Sch Elect State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China Peking Univ Ctr Quantum Informat Technol Beijing 100871 Peoples R China

Secure precision time synchronization is important for applications of cyber-physical systems (CPSs). However, several attacks, especially the time delay attack (TDA), deteriorate the performance of the time synchronization system seriously. The multiple paths scheme is thought as an effective security countermeasure to decrease the influence of TDA. However, the effective secure combination algorithm is still missed for precision time synchronization. In this article, a secure combination algorithm based on the Dempster-Shafer (D-S) theory is proposed for the multiple paths method. Special optimizations are done for the combination algorithm to solve the potential problems due to untrusted evidence. Theoretical simulation shows that the proposed algorithm works much better than the fault-tolerant algorithm (FTA) and the attack detection method based on a single path. An experimental demonstration proves the feasibility and superiority of the proposed algorithm, where the time stability with 27.97, 1.57, and 1.12 ps at average time 1, 10, and 100 s is achieved under TDAs and local clock jumps. The proposed algorithm can be used to improve the security and resilience of many important synchronization protocols, such as network time protocol (NTP), precision time protocol (PTP), and two-way fiber-optic time transfer (TWFTT).

关键词： Synchronization Clocks Security Main-secondary Mathematical models Delays Evidence theory Standards Time-frequency analysis Sun combination algorithm delay attack Dempster-Shafer (D-S) theory security time synchronization two-way fiber-optic time transfer (TWFTT)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：