检索结果-内蒙古大学图书馆

Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第11期13卷 554-562页

作者： Austero, Lea D. Sison, Ariel M. Matias, Junrie B. Medina, Ruji P. Technol Inst Philippines Grad Programs Quezon City Philippines Emilio Aguinaldo Coll Sch Engn & Technol Manila Philippines Caraga State Univ Coll Comp & Informat Sci Butuan City Philippines

The creation and generation of schedules that are free of conflicts manually every academic semester present higher education institutions with a duty that is laborious and demanding of their resources. The course timetabling optimization, as an education timetabling problem, is a popular example of an NP-hard combinatorial problem. Numerous attempts have been made over the course of the past few decades to find a solution to this problem, but no one has yet developed a foolproof approach that can examine all alternatives to find the best method. The promising swarm-based optimization algorithm called whale optimization algorithm was heuristically enhanced in the present study and is called HEWOA. It was designed as a solution to the course timetabling problem discussed in the current study. HEWOA was able to generate an efficient timetable for the large dataset of 1700 events for an average time of 14.92 seconds only, with an average generation of 7.2 and a best time of 8.38 seconds. These results reveal that the performance of HEWOA was better than that of various hybrids of the Genetic algorithm that was compared in the present study.

关键词： Heuristics mutation optimization swarm timetabling whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Machine learning-based single-phase ground fault identification strategy for AC-DC transmission lines

引用

ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期223卷

作者： Wu, Shuo He, Baina Meng, Fantao Liu, Yang Lin, Xin Dai, Weihan Wei, Yuanlong Wang, Shuo Zhang, Dongjin Shandong Univ Technol Coll Elect & Elect Engn Zibo 255000 Peoples R China Shenyang Univ Technol Key Lab Special Machine & High Voltage Apparat Minist Educ Shenyang 110870 Peoples R China

In this paper, an improved single-phase grounding fault (SPGF) nature identification strategy based on machine learning is proposed. To solve the problem of conventional single-phase automatic reclosure cannot distinguishing the nature of SPGF in AC-DC hybrid lines, the fault phase voltage characteristics of AC lines with DC feed are analyzed, and the voltage harmonic energy feature vector is extracted. The whale optimization algorithm (WOA) designed by orthogonal experiment design (OED) is used to select the model parameters of the least square support vector machine (LSSVM), and a discriminative model for the fault properties of the hybrid transmission line is obtained. The simulation experiments based on PSCAD/EMTDC 4.5.0 platform are conducted for model training and fault identification performance testing, and the results verifies the effectiveness and feasibility of the proposed method. Compared with LSSVM, the fault identification strategy based on OED-WOALSSVM has a higher accuracy in fault identification. In addition, the accuracy of the model identification strategy under different fault locations, parallel compensation degree, transition resistance, and power system noise effects is also verified.

关键词： Single-phase grounding fault Identification strategy Fault phase voltage Harmonic energy whale optimization algorithm Least squares support vector machine

来源：评论

学校读者我要写书评

暂无评论

A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

引用

SENSORS 2021年第24期21卷 8423-8423页

作者： Hussain, Saddam Mustafa, Mohd Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Saeed, Faisal Al-rimy, Bander Ali Saleh Univ Technol Malaysia Sch Elect Engn Johor Baharu 81310 Malaysia Qassim Univ Coll Comp Dept Comp Sci Buraydah 51452 Saudi Arabia Birmingham City Univ Sch Comp & Digital Technol Birmingham B4 7XG W Midlands England Univ Teknol Malaysia Sch Comp Fac Engn Johor Baharu 81310 Malaysia

This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

关键词： theft detection in power consumption data NGBoost algorithm majority weighted minority oversampling technique algorithm whale optimization algorithm tree SHAP algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Synthesis of Unequally Spaced Linear Arrays under Multiple Constraints

引用

APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2022年第4期39卷 291-296页

作者： Zhao, Zhong-Hui Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao 266590 Shandong Peoples R China

This paper proposes a differential evolution modified with adaptive epsilon-constraint handling and whale optimization algorithm (epsilon-WOA-DE) for the synthesis of unequally spaced linear arrays under array layout constraints and array pattern characteristics constraints. In particular, the success history based adaptive differential evolution with linear population reduction (LSHADE) serves as the basic search engine in this study. To ensure the searching ability of LSHADE under multiple constraints, an adaptive epsilon-constraint handling technique is implemented in LSHADE, in which the epsilon level is adjusted dynamically to make the solution scalable to the feasible region when it is in the infeasible region. In addition, the WOA mutation is implemented in the LSHADE to enhance the local search capability. Two array synthesis examples with multiple constraints are chosen to demonstrate the effectiveness of the proposed algorithm. analysis of the epsilon-WOA-DE illustrate the superior capability of the proposed method.

关键词： adaptive epsilon-constraint handling array synthesis differential evolution unequally spaced linear arrays whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter

引用

JOURNAL OF ENERGY STORAGE 2023年 65卷

作者： Duan, Wenxian Song, Shixin Xiao, Feng Chen, Yuan Peng, Silun Song, Chuanxue Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

Battery life prediction is of great practical significance to ensure the safety and reliability of equipment. This paper proposes a new framework to realize battery state of health (SOH) estimation and remaining useful life (RUL) prediction. The variable forgetting factor online sequential extreme learning machine (VFOS-ELM) is used to estimate battery SOH, and particle filter (PF) algorithm used to predict battery RUL. To improve the estimation accuracy, a new nonlinear decline method, adaptive weight and Gaussian variation are used to improve the standard whale optimization algorithm (WOA) algorithm. And the improved IWOA algorithm is used for parameter optimization of the VFOS-ELM and PF algorithm. The extremely randomized trees (ERT) algorithm is used to obtain the features with high correlation with the available capacity to reduce the complexity of the model and improve the estimation accuracy. Compared with other methods, the proposed IWOA-VFOS-ELM algorithm has higher estimation performance and noise anti-interference ability. The MAE of APR-3 and APR -4 for SOH estimation are both within 0.12 %, RMSE are within 0.15 %, and IA are both higher than 99.9 %. Compared with PF algorithm, the RUL prediction accuracy obtained by IWOA-PF algorithm is improved by 7.143 %, 6.445 % and 15.094, respectively. In summary, the IWOA-PF algorithm proposed in this paper can be used to predict the battery RUL, and the prediction performance is better than the PF algorithm.

关键词： State of health Remaining useful life whale optimization algorithm Extremely randomized trees Online sequential extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

Non-linear static and dynamic buckling analysis of laminated composite cylindrical shell embedded in non-linear elastic foundation using the swarm-based metaheuristic algorithms

引用

EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2022年 91卷 1页

作者： Foroutan, Kamran Varedi-Koulaei, Seyyed Mojtaba Duc, Nguyen Dinh Ahmadi, Habib Shahrood Univ Technol Fac Mech & Mechatron Engn Shahrood Iran Hanoi Univ Engn & Technol UET Vietnam Natl Univ Dept Technol & Engn Construct & Transportat Hanoi Vietnam Nguyen Tat Thanh Univ NTT Inst High Technol Dist 4 Ho Chi Minh City Vietnam Sejong Univ Dept Civil & Environm Engn Natl Res Lab 209 Neungdong Ro Seoul 05006 South Korea

In this paper, by determining the optimal fiber's angles for maximum critical buckling load, the non-linear static and dynamic buckling analyses of laminated composite cylindrical shells resting on non-linear elastic foundation subjected to external loading are investigated. The laminated composite cylindrical with three, four, and five layers is considered. In the present study, first, the optimal fiber's angles are found using the swarm-based metaheuristic algorithms, then, the non-linear static and dynamic buckling analyses are carried out at optimum fiber directions. According to classical shell theory, von-Karman equation, and Hooke's law, the stressstrain relations are derived for the laminated composite cylindrical shell. Then, based on the Galerkin's method, the discretized motion equation is obtained. For the optimization problem, the design variables are ply angles, and the objective function is maximizing the critical buckling load. Four recently developed swarm-based metaheuristic algorithms including, grey wolf optimizer (GWO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and salp swarm algorithm (SSA), are utilized to solve these optimization problems. Based on the results, the optimization algorithms have improved the value of the critical buckling load significantly. Also, the WOA algorithm had better results than the others, while the GOA was the weakest.

关键词： Laminated composite cylindrical shell Non-linear elastic foundation Grey wolf optimizer whale optimization algorithm Grasshopper optimization algorithm Salp swarm algorithm Non-linear static and dynamic buckling

来源：评论

学校读者我要写书评

暂无评论

Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 168卷 1页

作者： Wu, Lifeng Huang, Guomin Fan, Junliang Ma, Xin Zhou, Hanmi Zeng, Wenzhi Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Yangling 712100 Shaanxi Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Henan Univ Sci & Technol Coll Agr Engn Luoyang 471003 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

Accurate estimation of pan evaporation (E-p) is of great significance to the development of agricultural irrigation systems and agricultural water resources management. The purpose of this study was to explore the applicability of coupling extreme learning machine (ELM) model with two new meta-heuristic algorithms, i.e. whale optimization algorithm (WOA) and flower pollination algorithm (FPA) for monthly E-p prediction. To achieve this goal, two hybrid models of WOAELM and FPAELM were developed for predicting monthly E-p in the Poyang Lake Basin of Southern China as a case study. Their performances were further compared with the differential evolution algorithm-optimized ELM (DEELM), improved M5 model tree (M5P) and artificial neural networks (ANN) models. Monthly climatic parameters, including maximum and minimum temperature (T-max and T-min), sunshine duration (n), relative humidity (RH), wind speed (U) and E-p from four weather stations in the basin from 2001 to 2015 were collected, those of which during 2001-2010 were used for model training and those during 2011-2015 for model testing. The obtained results showed that the hybrid FPAELM model exhibited the highest prediction accuracy at all the four stations, followed by the hybrid WOAELM model, both of which were superior to the other traditional models. Heuristic algorithms, especially FPA, are highly recommended for improving performance of standalone machine learning models. Compared with the combination of multi-meteorological elements, the combination of Tmax Tmin and extraterrestrial solar radiation achieved higher but still satisfactory prediction accuracy, with the absolute error less than 0.1 mm d(-1) averaged over the four stations. Tmax Tmin and extraterrestrial solar radiation were thus suggested to be used for monthly E-p estimation in this area considering the convenience of data acquisition.

关键词： Pan evaporation Extreme learning machine whale optimization algorithm Flower pollination algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel power quality enhancement scheme for three-phase differential boost inverter-based grid-connected photovoltaic system with repetitive and feedback linearizing control

引用

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第5期29卷

作者： Pati, Akshaya K. Sahoo, Nirod C. Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 752050 Odisha India

In grid-connected (GC) photovoltaic (PV) system with a three-phase voltage source inverter (VSI), one major requirement is that the DC-side voltage should be greater than the inverter AC-side voltage. In many PV installations, this is difficult to attain without an additional DC-DC converter. For such scenarios, a three-phase differential boost inverter, where input DC voltage can be less than AC-side voltage, is a promising configuration. For GC PV systems integrated with such power electronic switching converters, the power quality control also plays a significant importance. In this paper, a novel power quality control technique for such a GC PV system based on three-phase differential boost inverter has been proposed and evaluated. The proposed control system consists of three subsystem level control loops: (1) a novel control unit for reference voltage generation required for power quality improvement, (2) a double-loop control strategy with plug-in-type repetitive Proportional-Integral (PI) control for outer loop and feedback linearizing control for inner loop meant for tracking of boost inverter output reference voltages, and (3) another bidirectional DC-DC converter controller for battery operation. The performance of the proposed control algorithms has been tested under various scenarios, ie, power management, transition between GC to stand-alone (SA) modes and vice versa, and grid current compensation under unbalanced load, nonlinear load, and unbalanced and distorted grid voltage conditions. Extensive simulation and experimental results are reported for validating the proposed control technique.

关键词： bidirectional converter boost inverter feedback linearization photovoltaic system repetitive controller whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107538-107538页

作者： Guo, Xuanming Hu, Jiao Yu, Helong Wang, Mingjing Yang, Bo Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Peoples R China

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary whale optimization algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks.

关键词： Population initialization Hybrid fuzzy rough set whale optimization algorithm Gene data feature selection Multiclass classification

来源：评论

学校读者我要写书评

暂无评论

Inadequate load output diagnosis of ultra-supercritical thermal power units based on MIWOA multi-label random forest

引用

APPLIED THERMAL ENGINEERING 2023年 227卷

作者： Tang, Mingzhu Liang, Zixin Ji, Dongxu Yi, Jiabiao Peng, Zhonghui Huang, Yujie Wang, Jiachen Chen, Donglin Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518000 Peoples R China

Aiming at the problem of considerable economic losses caused by the large fluctuation range of response load and Automatic Generation Control (AGC) commanded load of ultra-supercritical thermal power units, an inadequate load output diagnosis model of multi-label random forest with multi-improved whale optimization algorithm (MIWOA-MLRF) is proposed. Thermal power units are a kind of high-dimensional, nonlinear, and complex industrial complex, which brings difficulty for conventional mechanistic models in comprehensively analyzing the inadequate output causes of the units. With the help of extensive data analysis and artificial in-telligence algorithms, a multi-label random forest (MLRF) for inadequate output cause analysis is constructed. To improve the accuracy of WOA for MLRF classification, a good point set strategy is used to optimize the initial population distribution of WOA, and an improved convergence factor is used to control the speed of WOA search. Three Gaussian simulation datasets with a different number of features and labels and three sets of thermal power operation data with varying periods are utilized. The test results show that the missing alarm rates (MAR) of MIWOA-MLRF are 0.301, 0.621, and 0.802 under three sets of Gaussian data tests, which are the smallest values among all analytical models. Under three sets of thermal power unit operation data, the average values of MIWOA-MLRF's MAR are 2.2%, 0.2%, and 1.5%, respectively, which have lower missing alarm rates compared with other algorithms. Furthermore, in the false alarm rate (FAR) comparison, the FAR of MIWOA-MLRF de-creases significantly compared to MLRF. In the optimization time comparison, the optimization time required for MIWOA-MLRF is about 1000 s, which saves 33% on average compared to the optimization time of other algo-rithms. The result indicates that MIWOA-MLRF can guarantee the same better real-time performance with reduced FAR and MAR of inadequate output cause classification.

关键词： Thermal power units Inadequate output Multi-label classification whale optimization algorithm Random forest

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：