检索结果-内蒙古大学图书馆

Reliability and efficiency enhancement of a radial distribution system through value-based auto-recloser placement and network remodelling

引用

Protection and Control of Modern Power Systems 2023年第1期8卷 1-14页

作者： Bratati Ghosh Ajoy Kumar Chakraborty Arup Ratan Bhowmik Department of Electrical Engineering National Institute of Technology AgartalaJiraniaTripura 799046India

The electric distribution system(EDS)is prone to faults leading to power *** present energy market demands that electricity utilities invest more in different measures to improve the performance of the *** approach proposed here details a composite dual-phased methodology to improve the reliability and efficiency of the power delivered by the *** the first phase,the optimal allocation of auto-reclosers(AR)is undertaken by employing a newly formulated *** determination of the total number and location for AR placement is based on the economic analysis of two factors,i.e.,AR investment-maintenance cost and total benefit earned in terms of reliability improvement due to AR *** analysis also takes into account the impact of power outages on different load types,the load growth rate,and the inflation ***,to enhance the efficiency of the AR-incorpo-rated EDS,the technique of Radial Distribution System Remodelling is employed in the second *** method searches for a radial configuration that delivers power at minimum line *** phases comprising complex combinatorial operations are aided by a fresh hybrid of the Sine Cosine algorithm,Krill Herd algorithm,and a genetic operator of Differential *** results obtained from its application on the IEEE 69-bus distribution test system prove the credibility of the suggested formulation.

关键词： Auto-recloser placement Load flow metaheuristic optimization algorithm Power loss minimization Reconfiguration Reliability assessment

来源：评论

学校读者我要写书评

暂无评论

Projectiles optimization: A Novel metaheuristic algorithm for Global optimization

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2020年第10期33卷 1924-1938页

作者： Kahrizi, M. R. Kabudian, S. J. Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran

metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.

关键词： Global optimization metaheuristic optimization algorithm Population-based algorithm Stochastic optimization

来源：评论

学校读者我要写书评

暂无评论

The competition among supply chains regarding environmental, social, and resilience aspects in a supply chain network design problem

引用

JOURNAL OF MODELLING IN MANAGEMENT 2025年第1期20卷 182-210页

作者： Mollashahi, Hossein Fakhrzad, Mohammad Bagher Nasab, Hasan Hosseini Zare, Hassan Khademi Yazd Univ Dept Ind Engn Yazd Iran Yazd Univ Dept Ind Engn Fac Engn Yazd Iran

Purpose - Nowadays, the topic of supply chain network design has gained significant attention as a critical area within the field of supply chain management. Researchers have dedicated their studies to exploring market competitiveness and pursuing greater benefits. This paper presents a multi-objective mathematical model aimed at designing a competitive and sustainable closed-loop supply chain network specifically for perishable products. The proposed model takes into account the competition between two supply chains, considering various factors such as environmental impacts, social performance and resilience. Design/methodology/approach - To address the competitive nature of this model, this study introduces a two-stage approach. In the initial stage, this study uses a game theory approach to determine equilibrium values for competitive decisions. This allows establishing a fair and balanced playing field between the two supply chains. Subsequently, due to the complexity of the proposed model, this study uses a multi-objective metaheuristic algorithm based on the Non-dominated Sorting Genetic algorithm. This algorithm serves as a robust computational tool to effectively solve the network design problem and optimize the desired objectives. Findings - To evaluate the effectiveness of the proposed model and solution approach, this study provides a numerical example. Through graphical and statistical analysis, this study assesses the performance of the model and its ability to achieve the desired objectives. The results and charts obtained demonstrate the strong performance and efficacy of the proposed model in effectively addressing the challenges associated with designing a competitive and sustainable closed-loop supply chain network for perishable products. Research limitations/implications - By considering the various factors at play and incorporating them into the model, this study aims to contribute to the existing body of knowledge in supply chain management. Th

关键词： Supply chain network Interchain competition Sustainability Resilience metaheuristic optimization algorithm Game theory

来源：评论

学校读者我要写书评

暂无评论

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

引用

Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

来源：评论

学校读者我要写书评

暂无评论

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

引用

NEURAL COMPUTING & APPLICATIONS 2022年第8期34卷 6307-6329页

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Charmo Univ Coll Med & Appl Sci Appl Comp Dept Sulaimany Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Informat Technol Sulaimany Iraq Univ Human Dev Coll Sci & Technol Dept Informat Technol Sulaimany Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle algorithm (MOWCA), Non-dominated Sorting Genetic algorithm (NSGA-II), and Multi-Objective Dragonfly algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms.

关键词： Multiobjective algorithms Multiobjective evolutionary algorithms MOLPB LPB Learner performance-based behavior algorithm optimization metaheuristic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Frequency stabilization of a hybrid three-area power system equipped with energy storage units and renewable energy sources

引用

IET RENEWABLE POWER GENERATION 2022年第15期16卷 3267-3286页

作者： Elsaied, Mohamed Mostafa Abdel Hameed, Walid Helmy Hasanien, Hany M. Ain Shams Univ Elect Power & Machines Dept Cairo Egypt

This paper presents a three-area system tied together with tie lines. During the intervals of power mismatch, the system frequency deviates beyond the nominal value with an oscillatory response and the system may go to instability mode. The main purpose of the control strategy is optimizing the frequency fluctuations of the three areas and the deviations in the three tie line powers. This is achieved by three controllers as follows: the Tilt-Integral-Derivative (TID) controller, the Fractional Order Proportional-Integral-Derivative (FOPID), and the Proportional-Integral-Derivative (PID) controller. The three controllers are optimized by a new metaheuristic optimization algorithm based on the jellyfish behaviour in the ocean called the Jellyfish Search (JS) Optimizer. To prove the algorithm validity, it is compared with previous optimization techniques that have been applied to the study field such as Grey Wolf optimization (GWO) algorithm and Genetic algorithm (GA). Furthermore, renewable energy sources are implemented in the system such as wind energy and photovoltaic based on real data. Finally, energy storage devices (ESDs) like superconducting magnetic energy storage (SMES), capacitor energy storage (CES), and battery energy storage (BES) are implemented to improve the system behaviour due to the intermittent behaviour in the renewable sources.

关键词： three-term control power generation control renewable sources control strategy previous optimization techniques energy storage devices instability mode Proportional-Integral-Derivative controller system frequency deviates optimisation Fractional Order Proportional-Integral-Derivative oscillatory response energy storage frequency fluctuations control system synthesis tie line powers metaheuristic optimization algorithm power mismatch metaheuristics Jellyfish Search Optimizer capacitor energy storage tie lines algorithm validity frequency control Genetic algorithm system behaviour battery energy storage frequency stabilization superconducting magnet energy storage three-area system battery storage plants Tilt-Integral-Derivative controller renewable energy sources energy storage units three-area power system Grey Wolf optimization algorithm jellyfish behaviour genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Simultaneous optimization of fuel type and exterior walls insulation attributes for residential buildings using a swarm intelligence

引用

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2022年第4期19卷 2809-2822页

作者： Moloodpoor, M. Mortazavi, A. Ege Univ Dept Mech Engn Izmir Turkey Ege Univ Grad Sch Nat & Appl Sci Izmir Turkey

A significant portion of energy sources in many countries is devoted to the residential sector requirements. A considerable part of this energy is used for heating residential buildings. Minimizing the total heating cost of these buildings has a significant impact on reducing the economic burden and energy consumption. The deterministic parameters in the computation of total heating cost are the fuel type, insulation material and insulation thickness. Since these parameters are not linearly independent from each other, in the current study for a more comprehensive approach, a simultaneous multi-variable optimization model including both discrete and continuous design variables is developed. To solve the acquired model, the recently developed non-deterministic swarm-based approach so-called Interactive Search algorithm is applied as the optimization method. The degree-day values dependent on the climatic conditions of seven different cities of Turkey are considered as case studies. Subsequently, achieved numeric outcomes are reported as the optimal total heating cost, fuel type (i.e., the sequential integer that indicates the type of fuel) and insulation layer's material and thickness for each selected climate condition. Moreover, the resultant payback period and saving cost for the acquired optimal condition is calculated and announced. In all studied cities, a combination of natural gas for fuel type and glass wool for insulation material is obtained as the optimal state. The acquired optimal total heating cost values are consistent with the conventional approach results by an average deviation of 6.67e-3%, which reveals the proposed methodology works well in solving the problem.

关键词： Energy saving Insulated wall system metaheuristic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Simultaneous structural and operating optimization of process flowsheets combining process simulators and metaheuristic techniques: The case of solar-grade silicon process

引用

COMPUTERS & CHEMICAL ENGINEERING 2020年 140卷 106946-106946页

作者： Hernandez-Perez, Luis G. Ramirez-Marquez, Cesar Segovia-Hernandez, Juan G. Ponce-Ortega, Jose M. Univ Michoacana Chem Engn Dept Francisco J Mujica S-NEdificio V1Ciudad Univ Morelia 58060 Michoacan Mexico Univ Guanajuato Dept Ingn Quim Div Ciencias Nat & Exactas Campus GuanajuatoNoria Alta S-N Guanajuatogto 20256 Mexico

This paper presents a new optimization approach for the simultaneous structural optimization of process flowsheets with the operating conditions through combining process simulators with metaheuristic tech-niques. The proposed approach allows optimization of a superstructure in process simulators and reduce the computation time. A superstructure for different configurations for producing solar-grade silicon is considered, which includes three different configurations for solar-grade silicon production (Siemens Pro-cess, Intensified FBR Union Carbide Process, and Hybrid Process). The operating conditions with major impact in the performance of each of the proposed configuration were considered as decision variables. The improved multi-objective differential evolution (I-MODE) algorithm was selected as search method from others metaheuristic techniques because its efficiency to solve multi-objective problems in a short central process unit (CPU) time. The optimization algorithm consists in linking the process simulator software Aspen PlusTM with the metaheuristic technique. The results offered attractive options for the considered objective functions in the addressed case study. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Structural optimization through process simulators Optimal selection and design Sola-grade silicon production process metaheuristic optimization algorithm Simultaneous simulation in multi-case configuration process

来源：评论

学校读者我要写书评

暂无评论

Proactive dam operation based on inflow prediction by modified long short-term memory for improving resilience

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Lee, Eui Hoon Chungbuk Natl Univ Sch Civil Engn Cheongju 28644 South Korea

The operation of dams can have a significant downstream impact, causing major flood damage. For efficient dam operation, it is essential to accurately predict the inflow using deep learning such as a long short-term memory (LSTM). The modified LSTM (MLSTM) with new optimizers combined with a metaheuristic optimization algorithm was suggested. The MLSTM were trained using rainfall, water level, and discharge of the Yongdam Dam from 2010 to 2019 as input data and the inflow of the Daecheong Dam as output data. Normalization and time lagged cross correlation (TLCC) were applied as data preprocessing. The inflow of the Daecheong Dam in 2020 was predicted using rainfall, water level, and discharge of the Yongdam Dam. Compared to LSTM, MLSTM showed an accuracy improvement of about 63.85-83.34% based on the mean square error (MSE), which is a loss function. The results of proactive dam operation were calculated based on the predicted inflow considering TLCC. Compared to the existing operation, the peak water level of the reservoir was reduced from 77.54 m to 77.17 m, and the resilience was improved from 0.9225 to 0.9623. New dam operation can maintain low water levels of reservoir and improve resilience to prevent flood damage.

关键词： Modified long short-term memory metaheuristic optimization algorithm Harmony search Inflow prediction Proactive dam operation Resilience

来源：评论

学校读者我要写书评

暂无评论

An arithmetic optimization algorithm with balanced diversity and convergence for multimodal multiobjective optimization

引用

SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Zhang, Ruyue Mao, Shuhua Zhao, Shangrui Liu, Chang Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Yantai Huang Bohai New Area Nat Resources & Planni Yantai 265500 Peoples R China

Multimodal multiobjective optimization problems are widely prevalent in real life. Addressing these challenges is crucial as they directly impact the efficiency and effectiveness of solutions across various domains. This paper proposes a novel Multi-Modal Multi-Objective Arithmetic optimization algorithm (MMOP-AOA), aimed at achieving a high balance between diversity and convergence in both decision and objective spaces. Arithmetic optimization algorithm (AOA) is a highly competitive metaheuristic optimization algorithm with strong exploration and exploitation capabilities. MMOP-AOA extends the AOA for the first time to solve multimodal multiobjective problems, with the following ideas: Firstly, a new exploration and exploitation strategy (NBC-NEE) is designed based on the characteristics of *** strategy utilizes Neighborhood-Based Clustering (NBC) to partition the decision space into multiple clusters, aiding MMOP-AOA in capturing more equivalent Pareto subsets (ePSs). Secondly, a convergence and diversity balance mechanism (CDBM) is developed. This mechanism involves comparing the convergence indicator and diversity indicator to select different mutation strategies. Thirdly, an improved crowding distance (ICD) is proposed to address the deficiencies of existing special crowding distance measures. The effectiveness of CDBM and ICD is demonstrated in the paper through experiments on 22 benchmark functions from CEC-2019 and a real-world problem of signal timing optimization at road intersections. The research also reveals that compared to four other advanced multimodal multiobjective optimization algorithms, MMOP-AOA exhibits superior search capability and stability. Furthermore, MMOP-AOA utilizes Neighborhood-Based Clustering (NBC) to partition the decision space into multiple clusters, aiding MMOP-AOA in capturing more equivalent Pareto subsets (ePSs) and provides a theoretical framework for other metaheuristic optimization algorithms to tackle multimodal multi

关键词： Multimodal optimization Arithmetic optimization algorithm Multiobjective optimization metaheuristic optimization algorithm

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：