检索结果-内蒙古大学图书馆

Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid multi-objective genetic algorithm

IEEE ACCESS 2019年 7卷 158753-158769页

作者： Ogbemhe, John Mpofu, Khumbulani Tlale, Nkgatho Tshwane Univ Technol Dept Ind Engn ZA-0183 Pretoria West South Africa

The problem of trajectory planning is relevant for the proper use of costly robotic systems to mitigate undesirable effects such as vibration and even wear on the mechanical structure of the system. The objective of this study is to design trajectories that are devoid of collision, velocity, acceleration, jerk and snap discontinuities so that the cycle time required to complete the process can be reduced. The trajectory design was constructed for all the six joints, using a 9th order Bezier curve to accommodate the ten boundary conditions required to satisfy the continuity constraints for joints displacement, velocity, acceleration, jerk and snap. The scheme combines the multi-objective genetic algorithm and the multi-objective goal attainment algorithm to solve the problem of total tracking error reduction during arc welding. The use of a hybrid multi-objective algorithm shows an improved average spread, average distance, number of iteration and computational time. Also, it can be concluded from the constraints studied, that the optimal path in terms of the robots dynamic constraints can achieve the expected tracking ability in terms of the optimal joint angles, velocities, acceleration, jerk, snap and torque.

关键词： Industrial robots optimization multi-objective genetic algorithm trajectory planning jerk snap

来源：评论

学校读者我要写书评

暂无评论

Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine

引用

ENERGY 2022年第PartA期239卷 121889-121889页

作者： Rajkumar, Sundararajan Das, Arnab Thangaraja, Jeyaseelan Sri Sivasubramaniya Nadar Coll Engn Chennai 603110 Tamil Nadu India Vellore Inst Technol Sch Mech Engn Vellore 632014 Tamil Nadu India

Biodiesel usage is practically restricted as a blended supplement with fossil diesel. In the current study, the authors have attempted to arrive at the optimal biodiesel blend concentrations for an automotive engine. Here, the artificial neural network and genetic algorithm are coupled with phenomenological combustion modelling for the efficient operation of biodiesel blends. The engine experiments are conducted with diesel and diesel-biodiesel blends namely jatropha, and karanja consisting of 120 data points each. This set of data are used for the ANN development and validation. A multi-layer perceptron network is trained by the experimental data for predicting the engine parameters. The Nash Sutcliffe coefficient of efficiency values for the ANN predicted parameters are close to 1, indicating a close prediction. The ANN model predicted the engine output parameters with low values of mean square error, mean square relative error, mean absolute percentage error and standard error of prediction. Optimum values of biodiesel blend fraction, engine speed, brake mean effective pressure, injection pressure and timing are obtained using a multi-objective genetic algorithm. The optimised blend concentration is found to be-20% and-40% for satisfying the different objectives concerning the overall engine characteristics. Finally, the outputs for the optimised parameters are compared to the validated multi-zone model predictions within the maximum error of-3% and 7.9% for performance and emission parameters respectively. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Artificial neural networks Biodiesel blends multi-zone model Optimization Emissions

来源：评论

学校读者我要写书评

暂无评论

Distributed Data Real-Time Transaction Calculation Based on Collaborative Optimization and multi-objective genetic algorithm

引用

INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2024年第1期20卷 1页

作者： Liao, Li Chongqing City Vocat Coll Chongqing Peoples R China

Based on the above situation, this article elaborated on the methods that should be used to calculate the multi-information genetic algorithm (GA) in the current situation. The article mainly compared the non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) with elite strategy. By measuring the solving speed and quality of the two algorithms, it was found that the NSGA-II had a greater advantage. Based on the NSGA-II, optimization processing was carried out. The NSGA-II was compared before and after optimization. After analyzing 48 data samples, it was found that the results of the NSGA-II before and after optimization showed that the algorithm tended to be more stable after optimization, thus indicating that the improved data was more accurate. The results indicated that the NSGA-II was necessary for its improvement, and its results were also reasonable.

关键词： Distributed Data MOPSO algorithm multi-objective genetic algorithm NSGA-II algorithm Real-Time Transaction Computing

来源：评论

学校读者我要写书评

暂无评论

Optimal Test Points Selection Based on multi-objective genetic algorithm

引用

Journal of Electronic Science and Technology of China 2009年第4期7卷 317-321页

作者： Yong Zhang Xi-Xiang Chen Guan-Jun Liu Jing Qiu Shu-Ming Yang College of Mechatronical Engineering and Automation National University of Defense Technology Changsha 410073 China

A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.

关键词： Design for testability multi-objective genetic algorithm system testing test points selection.

来源：评论

学校读者我要写书评

暂无评论

Optimization of Aqueous Two Phase Extraction of Proteins from Litopenaeus Vannamei Waste by Response Surface Methodology Coupled multi-objective genetic algorithm

引用

CHEMICAL PRODUCT AND PROCESS MODELING 2020年第1期15卷

作者： Pandian, P. Saravana Selvan, S. Sindhanai Subathira, A. Saravanan, S. Natl Inst Technol Dept Chem Engn Tiruchirappalli 620015 Tamil Nadu India

Waste generated from industrial processing of seafood is an enormous source of commercially valuable proteins. One among the underutilized seafood waste is shrimp waste, which primarily consists of head and carapace. Litopenaeus vannamei (L. vannamei) is the widely cultivated shrimp in Asia and contributes to 90 % of aggregate shrimp production in the world. This work was focused on extraction as well as purification of value-added proteins from L. vannamei waste in a single step aqueous two phase system (ATPS). Polyethylene glycol (PEG) and trisodium citrate system were chosen for the ATPS owing to their adequate partitioning and less toxic nature. Response surface methodology (RSM) was implemented for the optimization of independent process variables such as PEG molecular weight (2000 to 6000), pH (6 to 8) and temperature (25 to 45 degrees C). The results obtained from RSM were further validated using a multi-objective genetic algorithm (MGA). At the optimized condition of PEG molecular weight 2000, pH 8 and temperature 35 degrees C, maximum partition coefficient and protein yield were found to be 2.79 and 92.37 %, respectively. Thus, L. vannamei waste was proved to be rich in proteins, which could be processed industrially through cost-effective non-polluting ATPS extraction, and RSM coupled MGA could be a potential tool for such process optimization.

关键词： Litopenaeus vannamei waste aqueous two phase system polyethylene glycol trisodium citrate response surface methodology multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Design of a Data-Driven Control System using a multi-objective genetic algorithm

引用

IFAC-PapersOnLine 2019年第29期52卷 310-313页

作者： Takuya Kinoshita Toru Yamamoto Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima Japan

In recent years, control design schemes for directly calculating control parameters from operational data have been realized and include the virtual reference feedback tuning (VRFT) method and the fictitious reference iterative tuning (FRIT) method. They were designed for objects that have a linear system. However, many objects in industry are nonlinear; hence, it is challenging to obtain good control performance by only applying fixed PID controllers. In this study, multiple linear systems as objects using multiple linear controllers are investigated. Specifically, it is necessary to solve two optimization problems of (i) the number of controllers (ii) the control parameters of each controller, and it is solving by using multi-objective genetic algorithm (MOGA) in this research.

关键词： multi-objective genetic algorithm data-driven control VRFT

来源：评论

学校读者我要写书评

暂无评论

Time-Cost Trade-off Problem under Uncertainty Incorporating multi-objective genetic algorithm and Fuzzy Sets Theory

Time-Cost Trade-off Problem under Uncertainty Incorporating ...

引用

2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management(2010年IEEE第17届工业工程与工程管理国际学术会议)

作者： C.H.Ding W.Wei X.Wei S.Fujimura Graduate School of Information Production and SystemsWaseda UniversityKitakyusyuJapan School of E Graduate School of Information Production and SystemsWaseda UniversityKitakyusyuJapan

The time-cost trade-off problem (TCTP) is an important branch in the project scheduling problem. However, the duration and cost of each activity could change stochastically as a result of uncertain factors. To meet the needs of real projects, an improved approach based on trapezoid fuzzy numbers is applied to estimate the uncertainty of time and cost. And then α-cut method is applied to decide the risk level. Furthermore, improved crossover and mutation methods for multiobjective genetic algorithm (MOGA) are used to make a large-scale computation possible. The efficiency of the proposed approach is verified by comparison with previous researches. In addition, economic analysis skill of finance cost is integrated into the new model to provide greater flexibility to managers when making decisions. Finally, time-cost tables under different risk levels for case examples are given and the advantages are investigated based on computation results.

关键词： Finance cost Fuzzy sets theory multi-objective genetic algorithm Project scheduling Risk management Time-cost trade-off problem

来源：评论

学校读者我要写书评

暂无评论

Optimization of Combined Scroll Profile Based on multi-objective genetic algorithm

Optimization of Combined Scroll Profile Based on Multi-objec...

引用

International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Tao Liu Fuyong Hou Lanzhou University of Technology

This In the design of scroll compressor,in order to improve the performance of the combined profile by involute of circle and high order curve,the mathematical model was established which takes the stroke volume and area utilization coefficient as the objective *** optimize it by the MATLAB genetic algorithm toolbox and have a contrast of the original parameters with the optimized *** optimal result shows that the result after optimization has higher stroke volume and area utilization coefficient.

关键词： combined profile stroke volume area utilization coefficient multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization design and evaluation of shift point for electric bus based on multi-objective genetic algorithm

Optimization design and evaluation of shift point for electr...

引用

第三届中国自动化学会车辆控制与智能化专业委员会学术年会

作者： Jingxu Zhang Yuchen Zhang Yulong Lei Yao Fu Xingzhong Li State Key Laboratory of Automotive Simulation and Control Jilin university Changchun China State Key Laboratory of Automotive Simulation and Control and Qingdao Automobile Research Institute

ISBN: (纸本)9781728126845

In this paper,an integrated simulation platform of electric bus based on AVL-Cruise and MATLAB is established to provide simulation basis for optimal design of shift *** extracting the objective function,design variables and constraint conditions,a mathematical model for solving the shift point problem with multiple objectives is *** NSGA-II algorithm was used to carry out multi-objective optimization design for the up-stop and down-stop points to obtain the pareto optimal *** optimal pareto solution was evaluated and analyzed by fuzzy comprehensive evaluation method,and the optimal results of shift MAP based on three cycle conditions were *** paper evaluates and analyzes the optimal results of shift MAP based on working conditions from four aspects of power performance,economy,driving performance and braking energy recovery.

关键词： electric bus mechanical automatic transmission multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Time-Cost Trade-off Problem under Uncertainty Incorporating multi-objective genetic algorithm and Fuzzy Sets Theory

Time-Cost Trade-off Problem under Uncertainty Incorporating ...

引用

第17届工业工程与工程管理国际学术会议(IE&EM2010)

作者： S.Fujimura Graduate School of Information Production and SystemsWaseda UniversityKitakyusyuJapan

The time-cost trade-off problem(TCTP) is an important branch in the project scheduling problem. However,the duration and cost of each activity could change stochastically as a result of uncertain *** meet the needs of real projects,an improved approach based on trapezoid fuzzy numbers is applied to estimate the uncertainty of time and *** thenα-cut method is applied to decide the risk ***,improved crossover and mutation methods for multi-objective genetic algorithm(MOGA) are used to make a large-scale computation *** efficiency of the proposed approach is verified by comparison with previous researches. In addition,economic analysis skill of finance cost is integrated into the new model to provide greater flexibility to managers when making ***,time-cost tables under different risk levels for case examples are given and the advantages are investigated based on computation results.

关键词： Finance cost Fuzzy sets theory multi-objective genetic algorithm Project scheduling Risk management Time-cost trade-off problem

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：