检索结果-内蒙古大学图书馆

Application of multi-objective optimization techniques to improve the aerodynamic performance of a tunnel ventilation jet fan

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2015年第1期229卷 91-105页

作者： Kim, Joon-Hyung Kim, Jin-Hyuk Yoon, Joon-Yong Choi, Young-Seok Yang, Sang-Ho Hanyang Univ Dept Mech Engn Seoul 133791 South Korea Korea Inst Ind Technol Thermal & Fluid Syst R&D Grp Cheonan Si 331822 Chungcheongnam South Korea Samwon E&B Technol Res Ctr Kyonggi Do South Korea

This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged Navier-Stokes equations with a shear stress transport turbulence model were solved. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi-objective optimization was carried out to improve the aerodynamic performance. A response surface approximation surrogate model was constructed for each objective function based on numerical solutions obtained at specified design points. The non-dominated sorting genetic algorithm with a local search procedure was used for multi-objective optimization. The tradeoff between the two objectives was determined and described with respect to the Pareto-optimal solutions. Based on the analysis of the optimization results, we propose an optimization model to satisfy the objective function. Finally, to verify the performance, experiments with the base model and the optimization model were carried out.

关键词： Jet fan tunnel ventilation multi objective optimization computational fluid dynamics experiment

来源：评论

学校读者我要写书评

暂无评论

EBST: An Evolutionary multi-objective optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第6期18卷 2384-2393页

作者： Yaghoobi, Hanif Babaei, Esmaeil Hussen, Bashdar Mahmud Emami, Ali Univ Tabriz Sch Nat Sci Dept Anim Biol Tabriz 51368 East Azerbaijan Iran Univ Tabriz Inst Environm Tabriz 51368 East Azerbaijan Iran Hawler Med Univ Coll Pharm Erbil 44000 Iraq

Ovarian cancer is the deadliest gynecologic malignancy, mainly due to limitations in early diagnosis. With advances in high-throughput technologies, research interest in identifying novel and customized tumor biomarkers for early detection and diagnosis is rapidly growing. Here we introduce a new tool called EBST to select microRNAs with biomarker potency in ovarian cancer. This tool has pre-processing options and Its core is the use of Modified multi objective Imperialist Competitive Algorithm and six objective functions based on the classifier performance/structure evaluation, clustering error and mRMR filter. In this paper, we used the FDR filter in the pre-processing stage and considered five objective functions, four of which relate to the l(1)-SVM classifier performance and one to the average mRMR ranking. The proposed method has identified 11 microRNAs including hsa-miR-6784-5p, hsa-miR-1228-5p, hsa-miR-8073, hsa-miR-6756-5p, hsa-miR-1307-3p, hsa-miR-4697-5p, hsa-miR-3663-3p, hsa-miR-328-5p, hsa-miR-1228-3p, hsa-miR-6821-5p, hsa-miR-1268a. Data classification by the proposed model showed 100 percent sensitivity, 99.38 percent specificity, 99.69 percent accuracy and 99.39 percent positive predictive value. In comparison with routine state-of-the-art methods, superiority of our method was confirmed. The biological evaluation of selected microRNAs using bioinformatics tools and published articles confirms their role in cancer signaling pathways. The tool and its MATLAB code are freely available at https://***/hanif-y.

关键词： Cancer Bioinformatics Biomarkers Linear programming optimization Classification algorithms Support vector machines Bioinformatics feature selection microRNAs with biomarker potency multi objective optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization framework complying IEEE 802.15.6 communication standards for wireless body area networks

引用

WIRELESS NETWORKS 2020年第6期26卷 4339-4362页

作者： Choudhary, Amit Nizamuddin, M. Zadoo, Manish Sachan, Vibhav Kumar Jamia Millia Islamia Dept Elect & Commun Engn New Delhi India ABES Engn Coll Dept Elect & Commun Engn Ghaziabad India KIET Grp Inst Dept Elect & Commun Engn Ghaziabad India

Wireless body area network (WBAN) routing protocols are primarily designed for improvement of network performance parameters such as network lifetime, throughput and latency. However, these designs do not take into account the health hazards posed due to WBAN radiation. The focus of implementing IEEE 802.15.6 standards is to develop an energy-efficient, reliable and health-conscious wireless communication system around human body. This paper presents the implementation of a clustering-based routing protocol for WBAN that targets in achieving overall optimization of WBAN performance parameters including network lifetime, throughput, latency, node signal power and specific absorption rate (SAR) of human body for emf radiation. The suggested protocol applies a multi-objective NSGA-II optimization heuristic for cluster formation while taking into account various IEEE 802.15.6 standard constraints. A unique chromosome structure has been proposed for NSGA-II population in which each chromosome represents a random network topology for determining the node clusters, cluster heads and node transmission power. Furthermore, three system-level models for conducting multi-objective fitness evaluation of each chromosome have been proposed. These models predict the global transmission energy consumption;the average received signal strength (RSSI) and end to end network latency respectively. The developed NSGA-II model evolves into a non-dominated set of Pareto-optimal network topologies that renders the desired objectives of minimization of network energy consumption and network latency besides maximization of average RSSI. Enhancement in RSSI further leads to a tremendous improvement in the throughput rate. The node transmission power specifications of Pareto-optimal network topologies meet the standard SAR constraints too. The performance results shows that the suggested protocol strictly meets IEEE 802.15.6 compliance.

关键词： Wireless body area network Routing protocol multi objective optimization NSGA-II Clustering Network lifetime Throughput

来源：评论

学校读者我要写书评

暂无评论

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

引用

APPLIED SOFT COMPUTING 2015年 35卷 766-785页

作者： Xiang, Yi Zhou, Yuren Sun Yat Sen Univ Sch Data Sci & Comp Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510275 Guangdong Peoples R China

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： multi objective optimization multi colony model Artificial bee colony algorithm Migration strategy Friedman test

来源：评论

学校读者我要写书评

暂无评论

Exergetic, economic, and environmental evaluations and multi-objective optimization of a combined molten carbonate fuel cell-gas turbine system

引用

APPLIED THERMAL ENGINEERING 2015年 77卷 1-11页

作者： Mamaghani, Alireza Haghighat Najafi, Behzad Shirazi, Ali Rinaldi, Fabio Politecn Milan Scuola Ingn Ind I-29100 Piacenza Italy Politecn Milan Dipartimento Energia I-20156 Milan Italy UNSW Sch Mech & Mfg Engn Kensington NSW 2052 Australia

The principal goal in the present work is to model a molten carbonate fuel cell-gas turbine (MCFC-GT) hybrid plant from energetic, exergetic, economic and environmental standpoints and to optimize the system through a multi-objective optimization scheme. Two conflicting objectives including exergetic efficiency and total cost rate of the system are introduced for multi-objective optimization. TOPSIS decision-making method is employed to determine the system final optimum design, leading to an overall exergetic efficiency of 51.7% and the total cost of 0.324 million USD per year. Moreover, a sensitivity analysis of the Pareto frontier to fuel unit cost and effective interest rate has been performed to investigate the variation of objective functions with economic parameters. Finally, a sensitivity analysis on the optimization results was performed for some of the key parameters, revealing the fact that operating pressure of the system has the highest impact on the exergetic efficiency compared to the other operating parameters. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Molten carbonate fuel cell Gas turbine Exergy Economic Environmental multi objective optimization

来源：评论

学校读者我要写书评

暂无评论

Dynamic multi-objective optimization for multi-period emergency logistics network

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第6期37卷 8471-8481页

作者： Wang, Yadong Shi, Quan Hu, Qiwei Army Engn Univ PLA Dept Equipment Command & Management Shijiazhuang Hebei Peoples R China

In order to solve the problem of multi-period material supply in emergency logistics network, a dynamic multi-objective optimization mathematical model with constraints is constructed. The model takes the minimum cost and the maximum fill rate of demands as the objectives, and takes the location of distribution centers and the allocation of material as the decision variables. A dynamic self-adaptive multi-objective differential evolution algorithm is proposed to solve the mathematical model, and the feasible non-dominated solutions of the model are obtained. In the improved algorithm, on the one hand, a new environment change detect operator and a new environment change response strategy are adopted so that the traditional static optimization algorithm can be used to solve the dynamic optimization problem. On the other hand, the improved algorithm adopts adaptive mutation strategy to improve the ability of global exploration and local exploitation. Case study shows that the improved strategy greatly improves the performance of the algorithm, and can solve the dynamic multi-objective optimization problem effectively.

关键词： Emergency logistics network multi objective optimization dynamic optimization evolutionary algorithm self-adaption

来源：评论

学校读者我要写书评

暂无评论

multi objective geometric optimization of phase change material based cylindrical heat sinks with internal stem and radial fins

引用

THERMAL SCIENCE AND ENGINEERING PROGRESS 2018年 5卷 238-251页

作者： Sridharan, Srinivas Srikanth, R. Balaji, C. Indian Inst Technol Madras Dept Mech Engn Heat Transfer & Thermal Power Lab Chennai 600036 Tamil Nadu India

This work presents the results of the multi objective geometric optimization of a phase change material (PCM) based cylindrical heat sink with thermal conductivity enhancers (TCEs) in the form of an internal stem with radial fins. The effect of change in the geometric distribution of the TCEs on the performance of the heat sink was studied while maintaining the total volume of TCEs constant. The ratio of the volume of the TCEs to the cavity was also fixed constant at 10%. Initially, experiments were carried out on a cylindrical heat sink with a PCM fill ratio of 99%. A constant heat input of 6 W was applied at the bottom of the heat sink through an electrical heater. The PCM used is n-eicosane and the heat sink is made of aluminium. Numerically obtained results from ANSYS Fluent 15.0 were compared with in-house experimental results to determine the heat transfer coefficient from the walls of the heat sink. The heat sink and TCE parameters namely the diameter of the cavity, the height of the cavity, the diameter of the stem, the number of fins and the thickness of fins were treated as design variables. For a constant power level, volume of cavity and volume of TCEs, 25 different geometric configurations (initial population) were generated and the charging and discharging times were obtained as outputs by full three dimensional transient numerical simulations. Artificial Neural Networks (ANNs) were then used to create a fast forward model and multi objective optimization was carried out to obtain the optimal set of geometric configurations which simultaneously maximizes the charging time and minimize the discharging time. The optimal solutions thus obtained were validated with full numerical simulations and were also compared with the initial population. Additional insights into the mechanism of heat transfer and fluid flow were obtained for the problem under consideration. (C) 2017 Published by Elsevier Ltd.

关键词： Phase change material Cylindrical heat sinks Artificial neural networks multi objective optimization Thermal conductivity enhancers Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Modeling and Pareto based multi-objective optimization of wavy fin-and-elliptical tube heat exchangers using CFD and NSGA-II algorithm

引用

APPLIED THERMAL ENGINEERING 2017年 111卷 325-339页

作者： Damavandi, Mohammad Darvish Forouzanmehr, Mostafa Safikhani, Hamed Univ Tehran Coll Engn Sch Mech Engn Tehran Iran Arak Univ Fac Engn Dept Mech Engn Arak *** Iran

In this paper, a multi-objective optimization (MOO) of wavy fin-and-elliptical tube heat exchangers has been performed by using Computational Fluid Dynamics (CFD), Artificial Neural Network (ANN) of Group Method of Data Handling (GMDH) type, and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This multi-objective optimization is aimed at achieving maximum heat transfer and minimum pressure drop. For this purpose, the considered objective functions, Colbum factor (j) and friction factor (f) are optimized with regards to the design variables (four variables). The CFD results are validated by means of experimental findings. Polynomials of the GMDH type neural network are formed based on the CFD results. These polynomials relate the objective functions to the design variables. Ultimately, the NSGA-II algorithm obtains the Pareto optimal points by using the input data from the neural network. From among the optimal points, several points with unique features are introduced and explained. The investigation of optimal points indicates that with a slight reduction in heat transfer, pressure drop can be reduced considerably. By combining and simultaneously using the CFD, neural network and NSGA-II optimization algorithm, very useful and valuable results are obtained;which otherwise couldn't be achieved without the mutual use of these techniques. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi objective optimization Wavy fin Elliptical tube GMDH NSGA-II

来源：评论

学校读者我要写书评

暂无评论

Sustainable H2 production/separation by integration of solid oxide electrolyzer, biomass gasifier and H2 separation membrane: A techno-economic/environmental evaluation and multi-objective optimization

引用

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 177卷 909-920页

作者： Zhongjie Fei Su Zhanguo Jianbo Lu Singh, Pradeep Kumar Dahari, Mahidzal Majdi, Hasan Sh. Ali, H. Elhosiny Bouzgarrou, Souhail Mohamed Nanjing Agr Univ Coll Vet Med Key Lab Anim Dis Diagnost & Immunol Minist Agr Nanjing Peoples R China Huainan Normal Univ Huainan 232038 Anhui Peoples R China Krirk Univ Int Coll Bangkok 10220 Thailand Nanning Normal Univ Guangxi Key Lab Human Machine Interact & Intellig Nanning 530001 Peoples R China GLA Univ Inst Engn & Technol Dept Mech Engn Mathura 281406 UP India Univ Malaya Fac Engn Deparment Elect Engn Kuala Lumpur 50603 Malaysia Mustaqbal Univ Coll Dept Chem Engn & Petr Ind Hilla 51001 Iraq King Khalid Univ Fac Sci Dept Phys POB 9004 Abha Saudi Arabia Jazan Univ Coll Engn Civil Engn Dept Jazan Saudi Arabia Sousse Univ Higher Inst Appl Sci & Technol Sousse Sousse Tunisia

Hydrogen as a clean fuel can simultaneously respond to the challenges of energy shortage and environmental issues, provided that it is produced in a clean and cheap way. In this research, a hydrogen production system based on the combination of a solid oxide electrolyzer with a gasifier and a hydrogen separation membrane has been studied from a techno-economic outlook. In order to achieve a proper approximation of the levelized cost of hydrogen (LCOH), an elaborated economic analysis has been performed considering all aspects, where the life of the hydrogen separation membrane (HSM) and the solid oxide electrolyzer (SOE) has been taken into account. Finally, the minimization of the levelized cost of hydrogen and the levelized carbon dioxide emission (LCE) was carried out through multi-objective optimization. The cost of selling hydrogen was determined according to the various payback times. According to the conducted optimization, it is possible to obtain the minimum levelized cost of hydrogen of 3.63 $/kg. This condition leads to the levelized emission of 10.17 kg CO2/kg H2. Also, the system has the ability to bear levelized CO2 emissions up to 2.92 kg CO2/kg H2, so that the levelized cost of H2 production is 6.45 $/kg. In the four optimal conditions obtained from the optimization, considering the payback time of 5 years, the cost of selling the produced H2 can be 3.64 $/kg, 6.45 $/kg, 5.98 $/kg, and 5.29 $/kg.

关键词： Gasification Hydrogen separation membrane Levelized cost of hydrogen multi objective optimization Solid oxide electrolyzer

来源：评论

学校读者我要写书评

暂无评论

Evolution strategies and multi-objective optimization of permanent magnet motor

引用

APPLIED SOFT COMPUTING 2012年第2期12卷 778-792页

作者： Andersen, Soren B. Santos, Ilmar F. Tech Univ Denmark Dept Mech Engn DK-2800 Lyngby Denmark

When designing a permanent magnet motor, several geometry and material parameters are to be defined. This is not an easy task, as material properties and magnetic fields are highly non-linear and the design of a motor is therefore often an iterative process. From an engineering point of view, we usually want to maximize the efficiency of the motor and from an economic point of view we want to minimize the cost of the motor. As these two things seldom go hand in hand, the goal is to find the best efficiency per cost. The scope of this paper is therefore to investigate the applicability of evolution strategies, ES to effectively design and optimize parameters of permanent magnet motors. Single as well as multi-objective optimization procedures are carried out. A modified way of creating the strategy parameters for the ES algorithm is also proposed and has together with the standard ES algorithm undergone a comprehensive parameter study for the parameters rho and lambda. The results of this parameter study show a significant improvement in stability and speed with the use of the modified ES version. To find the most effective selector for a multi-objective optimization, MOO, of the motor a performance examination of 4 different selectors from the group of programs called PISA has been made and compared for MOO of the efficiency and cost of the motor. This performance examination showed that the indicator based evolutionary algorithm, IBEA, and hypervolume estimation algorithm, HypE, selectors performed almost equally good on this MOO problem where the HypE selector only had a slightly better performance indicator. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Metaheuristic Permanent magnet motor optimization Evolution strategies multi objective optimization

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：