检索结果-内蒙古大学图书馆

Estimates of energy consumption in Turkey using neural networks with the teaching-learning-based optimization algorithm

引用

ENERGY 2014年第0期75卷 295-303页

作者： Uzlu, Ergun Kankal, Murat Akpinar, Adem Dede, Tayfun Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey Uludag Univ Fac Engn Dept Civil Engn TR-16059 Bursa Turkey

The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching-learning-based optimization) algorithm to estimate energy consumption in Turkey. Gross domestic product, population, import, and export data were selected as independent variables in the model. Performances of the ANN-TLBO model and the classical back propagation-trained ANN model (ANN-BP (teaching learning-based optimization) model) were compared by using various error criteria to evaluate the model accuracy. Errors of the training and testing datasets showed that the ANN-TLBO model better predicted the energy consumption compared to the ANN-BP model. After determining the best configuration for the ANN-TLBO model, the energy consumption values for Turkey were predicted under three scenarios. The forecasted results were compared between scenarios and with projections by the MENR (Ministry of Energy and Natural Resources). Compared to the MENR projections, all of the analyzed scenarios gave lower estimates of energy consumption and predicted that Turkey's energy consumption would vary between 142.7 and 158.0 Mtoe (million tons of oil equivalent) in 2020. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： teaching-learning-based optimization algorithm Energy consumption/demand Neural networks Turkey

来源：评论

学校读者我要写书评

暂无评论

Advanced optimal tolerance design of machine elements using teaching-learning-based optimization algorithm

引用

PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL 2014年第1期2卷 71-94页

作者： Rao, R. V. More, K. C. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

Tolerance design has become a very key issue in product and process development because of an informal compromise between functionality, quality, and manufacturing cost. The problem formulation becomes complex with simultaneous selection of design and manufacturing tolerances and the optimization problem is difficult to solve with the traditional optimization techniques. In this paper, a recently developed optimization algorithm called teaching-learning-based optimization (TLBO) is used for optimal selection of design and manufacturing tolerances with an alternative manufacturing process to obtain the optimal solution which is nearer to global optimal solution. Three problems are considered and these are: overrunning clutch assembly, knuckle joint assembly with three arms, and a helical spring. Out of these three problems, the problems of overrunning clutch assembly and knuckle joint assembly with three arms are multi-objective optimization problems and the helical spring problem is a single-objective problem. The comparison of the proposed algorithm is made with the genetic algorithm (GA), Non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective particle swarm optimization algorithm (MOPSO). It is found that the TLBO algorithm has produced better results when compared to those obtained by using GA, NSGA-II, and MOPSO algorithms.

关键词： tolerance design multi-objective optimization machine elements teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

teaching-learning-based optimization algorithm in Dynamic Environments

Teaching-Learning-Based Optimization Algorithm in Dynamic En...

引用

4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Zou, Feng Wang, Lei Hei, Xinhong Jiang, Qiaoyong Yang, Dongdong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China HuaiBei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

ISBN: (纸本)9783319037530;9783319037523

In many real word problems, optimization problems are nonstationary and dynamic. optimization of these dynamic optimization problems requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. In this paper, for the first time, a multi-swarm teaching-learning-based optimization algorithm (MTLBO) is proposed for optimization in dynamic environment. In this method, all learners are divided up into several subswarms so as to track multiple peaks in the fitness landscape. Each learner is learning from the teacher and the mean of his or her corresponding subswarm instead of the teacher and the mean of the class in teaching phase, and then learners learn from interaction between themselves in their corresponding subswarm in learning phase. Moreover, all subswarms are regrouped periodically so that the information exchange is made with all the learners in the class to achieve proper exploration ability. The proposed MTLBO algorithm is evaluated on moving peaks benchmark problem in dynamic environments. The experimental results show the proper accuracy and convergence rate for the proposed approach in comparison with other well-known approaches.

关键词： teaching-learning-based optimization algorithm Dynamic Environments Multi-swarm Moving Peaks Benchmark problem

来源：评论

学校读者我要写书评

暂无评论

Multi-pass turning process parameter optimization using teaching-learning-based optimization algorithm

引用

SCIENTIA IRANICA 2013年第3期20卷 967-974页

作者： Rao, R. Venkata Kalyankar, V. D. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

The primary objective in multi-pass turning operations is to produce products with low cost and high quality, with a lower number of cuts. Parameter optimization plays an important role in achieving this goal. Process parameter optimization in a multi-pass turning operation usually involves the optimal selection of cutting speed, feed rate, depth of cut and number of passes. In this work, the parameter optimization of a multi-pass turning operation is carried out using a recently developed advanced optimization algorithm, named, the teaching-learning-based optimization algorithm. Two different examples are considered that have been attempted previously by various researchers using different optimization techniques, such as simulated annealing, the genetic algorithm, the ant colony algorithm, and particle swarm optimization, etc. The first example is a multi-objective problem and the second example is a single objective multi-constrained problem with 20 constraints. The teaching-learning-based optimization algorithm has proved its effectiveness over other algorithms. (C) 2013 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Multi-pass turning process Parameter optimization teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Parameter optimization of machining processes using teaching-learning-based optimization algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第5-8期67卷 995-1006页

作者： Pawar, P. J. Rao, R. Venkata KK Wagh Inst Engn Educ & Res Nasik India SV Natl Inst Technol Surat 395007 Gujarat India

The optimum selection of process parameters plays a significant role to ensure quality of product, to reduce the machining cost and to increase the productivity of any machining process. This paper presents the optimization aspects of process parameters of three machining processes including an advanced machining process known as abrasive water jet machining process and two important conventional machining processes namely grinding and milling. A recently developed advanced optimization algorithm, teaching-learning-based optimization (TLBO), is presented to find the optimal combination of process parameters of the considered machining processes. The results obtained by using TLBO algorithm are compared with those obtained by using other advanced optimization techniques such as genetic algorithm, simulated annealing, particle swarm optimization, harmony search, and artificial bee colony algorithm. The results show better performance of the TLBO algorithm.

关键词： Abrasive water jet machining Grinding Milling Process parameter optimization teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimizing Tunnel Excavation: Intelligent algorithms for Accurate Overbreak Prediction

引用

MINING METALLURGY & EXPLORATION 2024年第5期41卷 2525-2538页

作者： Fattahi, Hadi Nejati, Hamid Reza Ghaedi, Hossein Arak Univ Technol Fac Earth Sci Engn Arak Iran Tarbiat Modares Univ Sch Engn Rock Mech Div Tehran Iran

Excavating tunnels has become a widespread practice in the modern world, driven by the need for efficient transportation, subterranean storage, and mineral supply. One challenge encountered during tunnel excavation is the overbreak (OB) phenomenon, particularly prominent when utilizing drilling and blasting techniques. OB poses a risk by increasing operational expenses and compromising workplace safety. Therefore, accurately predicting the occurrence of OB during tunnel excavation is crucial. While various methods exist to forecast OB, traditional approaches like experimental, analytical, numerical, and regression methods face limitations due to uncertainties in geological and geotechnical parameters. In this paper, the use of teaching-learning-based optimization (TLBO) and Firefly (FF) algorithms is proposed to predict OB, aiming to fully comprehend the physical and mechanical characteristics of the rock mass while considering uncertainties and optimizing project completion in terms of cost and time. The model was constructed using data from three case studies: an Indian mine;the Azad tunnel on the Tehran-North route in Alborz, Iran;and the underground coal mine Tarzareh, comprising 217 data points. Parameters affecting the OB phenomenon in this study include rock mass rating (RMR), specific drilling (SD), perimeter holes powder factor (PPF), and spacing to burden ratio of contour holes (S/B). The dataset was divided into two groups: 80% for training the model and 20% for testing the relationship. To evaluate the model, statistical indices such as squared correlation coefficient (R2), root mean square error (RMSE), and mean square error (MSE) were used. The validation results indicated that the TLBO and FF algorithms performed satisfactorily, demonstrating high accuracy and low error. This suggests that engineers, scientists, and practitioners can benefit from employing intelligent approaches in mining and rock mechanics-related operations, utilizing the accurate m

关键词： Tunnel excavation Overbreak phenomenon teaching-learning-based optimization algorithm Firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Probabilistic optimal planning in active distribution networks considering non-linear loads based on data clustering method

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第4期16卷 686-702页

作者： Ebrahimi, Hasan Rezaeian-Marjani, Saeed Galvani, Sadjad Talavat, Vahid Urmia Univ Fac Elect & Comp Engn Dept Power Engn Orumiyeh Iran

Renewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredictable nature of renewable energies, besides the effect of non-linear loads brings out serious planning and operating challenges for distribution systems. Basicly, harmonic distortion is a severe problem for both electric efficiency and power energy customers. This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non-linear loads using a multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm and the best solution is selected via the TOPSIS technique. In the proposed strategy, energy storage systems are optimally scheduled besides wind turbines, and reactive power compensators. Also, to use the distribution network more efficiently, an optimal network reconfiguration is applied. The wind turbine's output and load demands have probabilistic nature. The proposed scheme reduces the total harmonic distortion as well as total costs. The efficacy of the proposed management scheme is investigated using the IEEE standard 33 bus distribution network. Also, the performance of the multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm is compared with the multi-objective particle swarm optimization algorithm.

关键词： energy storage systems power system harmonics particle swarm optimisation reactive power multiobjective particle swarm optimization algorithm harmonic distortion renewable energies TOPSIS technique total harmonic distortion multiobjective individualized instruction mechanism distribution systems wind turbines power quality problems energy demands reactive power compensators teaching-learning-based optimization algorithm probabilistic optimal planning optimal network reconfiguration probabilistic nature electric efficiency data clustering method optimal scheduling strategy power energy customers nonlinear loads TOPSIS power supply quality power distribution planning power electronic devices IEEE standard 33 bus distribution network active distribution networks

来源：评论

学校读者我要写书评

暂无评论

A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第4期27卷 2962-+页

作者： Tefek, Mehmet Fatih Uguz, Harun Ahi Evran Univ Kaman Vocat Sch Dept Comp Technol Kirsehir Turkey Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

The economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey's 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems.

关键词： Hybrid optimization method gravitational search algorithm teaching-learning-based optimization algorithm economic dispatch power systems

来源：评论

学校读者我要写书评

暂无评论

A Novel Frequency and Voltage Control Method for Islanded Micro-grid 9

A Novel Frequency and Voltage Control Method for Islanded Mi...

引用

9th Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Akbari, Ehsan Mazandaran Univ Sci & Technol Fac Elect & Comp Engn Babol Iran

ISBN: (纸本)9781665405652

Two performance modes, connected to grid and grid independent, can be considered for microgrids. In connected mode, the grid voltage and frequency are controlled by the grid and the microgrid controls the exchange of electrical power with the grid. In a standalone mode where there is no connection between the microgrid and the main grid, the microgrid is responsible for regulating voltage and frequency. One of the critical issues of microgrids, in a separate state from the power system, is frequency and voltage control. In this paper, the improvement of microgrid control performance in the field of voltage and frequency is investigated. voltage and frequency control are investigated to improve the performance of the micro-grid. Different types of distributed generation (DG) have been used to investigate and improve voltage stability. A new combination of teaching-learning based optimization (TLBO) and harmonic search algorithm (HSA) has been used for optimal performance in islanded Microgrid. Performance optimization is accomplished by finding the optimal parameters for the droop coefficient of the distributed generation resources and the reliability of the wind units in order to reduce the cost of generated energy. optimization is defined as a multi-objective problem, and the objective functions are used to minimize fuel consumption of distributed generation and improve the voltage characteristic and microgrid stability of operating and protection constraints. System steady state frequency, reference frequency, droop coefficients and reference voltage of the distributed generation unit are considered as optimization variables. The proposed method is compared with other methods for the 33-bus microgrid using MATLAB software. The simulation results show that the proposed method improves the performance of the microgrid.

关键词： teaching-learning-based optimization algorithm Harmonic search algorithm Microgrid Islanded

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第3期51卷 1855-1868页

作者： Tian, Guangdong Hao, Nannan Zhou, MengChu Pedrycz, Witold Zhang, Chaoyong Ma, Fangwu Li, Zhiwu Shandong Univ Sch Mech Engn Minist Educ Key Lab High Efficiency & Clean Mech Mfg Jinan 250061 Peoples R China Shandong Univ Natl Demonstrat Ctr Expt Mech Engn Educ Jinan 250061 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Tsinghua Univ Sch Ind Engn Beijing 100084 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2G7 Canada Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts' subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify lambda-fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among lambda-fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of lambda-fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness.

关键词： Correlation Indexes Decision making Green design Safety Guidelines optimization Fuzzy Choquet integral grey comprehensive evaluation (GCE) multicriteria decision making (MCDM) teaching-learning-based 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：