检索结果-内蒙古大学图书馆

Input optimization of ANFIS typhoon inundation forecast models using a multi-objective genetic algorithm

JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 2018年 19卷 16-27页

作者： Ouyang, Huei-Tau Natl Ilan Univ 1Sec 1Shennong Rd Yilan 260 Yilan County Taiwan

Inundation forecast models based on a subset of data inputs are advantageous in their efficiency due to the rather fewer inputs needed to be processed. This type of models is nevertheless rare mainly because of the difficulty in selecting the appropriate combination of input variables. In this study, an innovative methodology is proposed to overcome this difficulty by integrating Adaptive Network-based Fuzzy Inference System (ANFIS) and multi objective genetic algorithm (MOGA). The three indices of the coefficient of efficiency (CE), relative peak error (RPE) and relative time shift error (RTS) are used to assess the performances of the models from various perspectives. Optimal combinations of data inputs for the ANFIS models are searched for by MOGA, and the three models with the best performances for each index are selected. Comparisons show that the optimal models obtained by the proposed methodology have better overall performances than the ARX and Nonlinear ARX models. Test results reveal that the optimal models preferably selected for a designated prediction lead time cannot maintain optimality under different prediction leads. This problem has been resolved by the use of a series of models, each optimized with respect to a designated prediction lead time using the same methodology. Test results show that the model series exhibits significant improvements under various prediction leads and thus effectively improves the accuracy of inundation level prediction.

关键词： Optimization ANFIS Model inputs multi-objective genetic algorithm Inundation Typhoon

来源：评论

学校读者我要写书评

暂无评论

RF design of APEX2 two-cell continuous-wave normal conducting photoelectron gun cavity based on multi-objective genetic algorithm

引用

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2019年 940卷 12-18页

作者： Luo, T. Feng, H. Filippetto, D. Johnson, M. Lambert, A. Li, D. Mitchell, C. Sannibale, F. Staples, J. Virostek, S. Wells, R. Lawrence Berkeley Natl Lab One Cyclotron Rd Berkeley CA 94720 USA Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China

High brightness, high repetition rate electron beams are key components for optimizing the performance of next generation scientific instruments, such as MHz-class X-ray Free Electron Laser (XFEL) and Ultra-fast Electron Diffraction/Microscopy (UED/UEM). In the Advanced Photo-injector EXperiment (APEX) at Berkeley Lab, a photoelectron gun based on a 185.7 MHz normal conducting re-entrant RF cavity, has been proven to be a feasible solution to provide high brightness, high repetition rate electron beam for both XFEL and UED/UEM. Based on the success of APEX, a new electron gun system, named APEX2, has been under development to further improve the electron beam brightness. For APEX2, we have designed a new 162.5 MHz two-cell photoelectron gun and achieved a significant increase on the cathode launching field and the beam exit energy. For a fixed charge per bunch, these improvements will allow for the emittance reduction and hence to an increased beam brightness. The design of APEX2 gun cavity is a complex problem with multiple design goals and restrictions, some even competing each other. For a systematic and comprehensive search for the optimized cavity geometry, we have developed and implemented a novel optimization method based on the multi-objective genetic algorithm (MOGA).

关键词： Photoelectron RF gun RF cavity design multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production

引用

ENERGY REPORTS 2020年 6卷 2988-2998页

作者： Elsoragaby, Suha Yahya, Azmi Mahadi, Muhammad Razif Nawi, Nazmi Mat Mairghany, Modather Elhassan, Sami Mustafa M. Kheiralla, A. F. Univ Putra Malaysia Fac Engn Dept Biol & Agr Engn Serdang 43400 Selangor De Malaysia Univ Khartoum Fac Engn Dept Agr & Biol Engn Khartoum Sudan Univ Teknol PETRONAS Inst Self Sustainable Bldg Seri Iskandar Perak Malaysia

Efficient use of energy in crops production will minimize greenhouse gas emission (GHG), prevent destruction of natural resources, and promote sustainable agriculture as an economical crop production system. The aim of this study is applying the multi-objective genetic algorithm MOGA to optimize the energy inputs and reduce the greenhouse gas emissions (GHG) for wetland rice production in Malaysia. The developed multi-objective genetic algorithm (MOGA) model, showed an excess of energy inputs used by the farmers more than the required energy by 37.8% and 40% for the transplanting and broadcast seeding methods. The potential of GHG emissions reduction by MOGA was computed as 95.89 and 236.13 kg CO2eq/ha. Nitrogen represents the highest contributor to the reduction of both, total energy input and total GHG emissions in the two cultivation methods transplanting and broadcast seeding methods. Despite lower consumption of inputs by MOGA, crop yield is estimated at 9.4 ton/ha in transplanting and 9.2 ton/ha in broadcast seeding, which is close to the region's maximum under current condition. The main finding that MOGA model showed an excess of energy inputs used and the potential of GHG emissions reduction was 19.6% and 46.37%. for the transplanting and broadcast seeding methods. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Energy analysis Greenhouse gas emissions (GHG) Optimization multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Tourists Initial Optimal Shunt Scheme Using multi-objective genetic algorithm

引用

WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 3517-3527页

作者： Hu, Tao Jin, Maozhu Lei, Xia Liao, Zhixue Ge, Peng Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu Sichuan Peoples R China

With the rapid development of tourism, it not only brings great economic benefits, but also causes some problems. Overcrowded visitors reduce tourists satisfaction and bring about negative impact on ecological environment. Designing a reasonable tourists initial shunt scheme can help the management of the scenic area to achieve the goal that balances the economic development and environment protection. In this paper, for the sake of alleviating congestion in scenic area, a tourists optimal shunt scheme is proposed. Instead of defining single optimal objective, the proposed scheme taking two objectives into account, which are minimizing the total load balance degree of the scenic spots and one spot's load degree. In order to estimate optimal shunt ratios, a multi-objective optimization based on genetic algorithm is used to find the Pareto solution. Then a simulation model is built to investigate and verify the scheme. Finally, a comparison analysis validates the efficiency of the model in mitigating the load of the scenic spots in Jiuzhai Valley.

关键词： Tourist shunt Optimization scheme multi-objective genetic algorithm Simulation

来源：评论

学校读者我要写书评

暂无评论

Workload Allocation in IoT-Fog-Cloud Architecture Using a multi-objective genetic algorithm

引用

JOURNAL OF GRID COMPUTING 2020年第1期18卷 43-56页

作者： Abbasi, Mahdi Pasand, Ehsan Mohammadi Khosravi, Mohammad R. Bu Ali Sina Univ Engn Fac Dept Comp Engn Hamadan Hamadan Iran Persian Gulf Univ Dept Comp Engn Bushehr Iran Shiraz Univ Technol Dept Elect & Elect Engn Telecommun Grp Shiraz Iran

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been considerably increased. The processing resources of IoT nodes cannot cope with such huge workloads. Processing parts of the workload in clouds could solve this problem, but the quality of services for end-users will be decreased. Given the latency reduction for end-users, the concept of processing in the fog devices, which are at the edge of the network has been evolved. Optimizing the energy consumption of fog devices in comparison with cloud devices is a significant challenge. On the other hand, providing the expected-quality of service in processing the requested workloads is highly dependent on the propagation delay between fog devices and clouds, which due to the nature of the distribution of clouds with the different workloads, is highly variable. To date, none of the proposed solutions has solved the problem of workload allocation given the criteria of minimizing the energy and delay of fog devices and clouds, simultaneously. This paper presents a processing model for the problem in which a trade-off between energy consumption and delay in processing workloads in fog is formulated. This multi-objective model of the problem is solved using NSGAII algorithm. The numerical results show that by using the proposed algorithm for workload allocation in a fog-cloud scenario, both of energy-consumption and delay can be improved. Also, by allocating 25% of the IoT workloads to fog devices, the energy consumption and delay are both minimized.

关键词： Fog computing Internet of Things multi-objective genetic algorithm Workload distribution Energy Delay

来源：评论

学校读者我要写书评

暂无评论

Springback control of sheet metal forming based on the response-surface method and multi-objective genetic algorithm

引用

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2009年第1-2期499卷 325-328页

作者： Liu Wei Yang Yuying Xing Zhongwen Zhao Lihong Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

An automated optimization method by the integration of the response-surface method and an optimization algorithm is presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, a multi-objective genetic algorithm is applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. The response-surface model is employed as a fast analysis tool to surrogate the time-consuming finite-element procedure in the iterations of the multi-objective genetic algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is more efficient than the traditional manual finite-element procedure and the 'trial and error' approach for springback controlling. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Springback Sheet metal Response-surface method multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending

引用

RENEWABLE ENERGY 2024年 225卷

作者： Rawat, Shweta Wagadre, Lokesh Kumar, Sanjay Indian Inst Technol BHU Varanasi Sch Biochem Engn Varanasi 221005 Uttar Pradesh India Bipin Tripathi Kumaon Inst Technol Biochem Engn Dept Dwarahat 263653 Uttaranchal India

Towards establishing low -carbon bio-economy, the energy -rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co -pyrolysis -based valorization of de -oiled microalgae and low -rank coal blend to generate H-2 and CH4 rich syngas. Pyrolysis kinetic models, KissingerAkahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (Ea). The gradual addition of microalgae (0-100%) in coal reduces E-a from 189.11-55.87 kJ/mol and 180.16-54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network -based multi -objective genetic algorithm (ANN-MOGA), respectively. Maximum H-2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 C-degrees) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate -13.8 C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co -pyrolysis approach to gain carbon -neutral energy with zero waste discharge.

关键词： De-oiled microalgae Co-pyrolysis Syngas Artificial neural network multi-objective genetic algorithm Hymethane carrying ratio

来源：评论

学校读者我要写书评

暂无评论

A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem

引用

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2-3期45卷 115-125页

作者： Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Li, Xinyu Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

In many real-world applications, processing times may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm based on immune and entropy principle to solve the multi-objective fuzzy FJSP. In this improved multi-objective algorithm, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.

关键词： fuzzy flexible job-shop scheduling multi-objective genetic algorithm Pareto-optimality immune and entropy principle

来源：评论

学校读者我要写书评

暂无评论

Turning of Carbon Fiber Reinforced Polymer (CFRP) Composites: Process Modeling and Optimization using Taguchi Analysis and multi-objective genetic algorithm

引用

MANUFACTURING LETTERS 2022年 33卷 29-40页

作者： Rob, S. M. Abdur Srivastava, Anil K. Univ Texas Rio Grande Valley 1201 W Univ Dr Edinburg TX 78539 USA

Carbon Fiber Reinforced Polymer (CFRP) composites have been widely used in aerospace, automotive, nuclear, and biomedical industries due to their high strength-to-weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties in non-oxidative atmospheres. Machining of CFRP composites has always been a challenge for manufacturers. In this research, a comparative study was performed between the optimal machining parameters of coated and uncoated carbide inserts obtained from the multi-objective genetic algorithm during turning of CFRP composites. It was found that coated carbide inserts provide lower tool wear and surface roughness, but higher cutting forces compared to those of uncoated carbide inserts during turning of CFRP composites. Taguchi Analysis was performed to investigate the effects of machining parameters (cutting speed, feed rate and depth of cut) on the output characteristics including cutting force, surface roughness and tool wear. The feed rate was found as the most significant machining parameter in turning of CFRP composites to minimize cutting force and surface roughness using both coated and uncoated carbide inserts. However, feed rate and cutting speed has been found as the most significant machining parameters for coated and uncoated carbide inserts respectively to minimize the tool wear. Regression Analysis has been performed to develop mathematical models for cutting force, surface roughness and tool wear as a function of cutting speed, feed rate and depth of cut. Higher R2 values and well fitted regression lines of normal probability plots in regression analysis indicate that the coefficients of mathematical models are statistically significant. The significance of this study is to emphasize the differences of performances between coated and uncoated carbide inserts during turning of CFRP composites in terms of cutting force, tool wear and surface roughness with the combination of different machining parameters (cu

关键词： CFRP composites Taguchi Analysis multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm

引用

BUILDING SIMULATION 2014年第6期7卷 661-669页

作者： Zhai, Zhiqiang (John) Xue, Yu Chen, Qingyan Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China Univ Colorado Boulder CO 80309 USA Purdue Univ Sch Mech Engn W Lafayette IN 47905 USA

Conventional designers typically count on thermal equilibrium and require ventilation rates of a space to design ventilation systems for the space. This design, however, may not provide a conformable and healthy micro-environment for each occupant due to the non-uniformity in airflow, temperature and ventilation effectiveness as well as potential conflicts in thermal comfort, indoor air quality (IAQ) and energy consumption. This study proposes two new design methods: the constraint method and the optimization method, by using advanced simulation techniques-computational fluid dynamics (CFD) based multi-objective genetic algorithm (MOGA). Using predicted mean vote (PMV), percentage dissatisfied of draft (PD) and age of air around occupants as the design goals, the simulations predict the performance curves for the three indices that can thus determine the optimal solutions. A simple 2D office and a 3D aircraft cabin were evaluated, as demonstrations, which reveal both methods have superior performance in system design. The optimization method provides more accurate results while the constraint method needs less computation efforts.

关键词： inverse modeling multi-objective genetic algorithm computational fluid dynamics predicted mean vote percent dissatisfied age of air

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：