检索结果-内蒙古大学图书馆

Handling swarm of UAVs based on evolutionary multi-objective optimization

PROGRESS IN ARTIFICIAL INTELLIGENCE 2017年第3期6卷 263-274页

作者： Ramirez-Atencia, Cristian R-Moreno, Maria D. Camacho, David Univ Autonoma Madrid C Tomas & Valiente 11 E-28049 Madrid Spain Univ Alcala De Henares Carretera Madrid BarcelonaKm 33 600 Madrid 28871 Spain

The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.

关键词： Unmanned aerial vehicles Mission planning Constraint satisfaction problems multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal position of rectangular vortex generator for heat transfer enhancement in a flat-plate channel

引用

ASME Summer Heat Transfer Conference

作者： Khan, Tariq Amin Li, Wei Zhang, Zhengjiang Du, Jincai Khan, Sadiq Amin Liu, Fu Zhejiang Univ Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Collaborat Innovat Ctr Adv Aeroengine Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Zhejiang Univ Dept Mat Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9780791857885

Heat transfer is a naturally occurring phenomenon which can be greatly enhanced by introducing longitudinal vortex generators (VGs). As the longitudinal vortices can potentially enhance heat transfer with small pressure loss penalty, VGs are widely used to enhance the heat transfer of flat-plate type heat exchangers. However, there are few researches which deal with its thermal optimization. Three dimensional numerical simulations are performed to study the effect of angle of attack and attach angle (angle between VG and wall) of vortex generator on the fluid flow and heat transfer characteristics of a flat-plate channel. The flow is assumed as steady state, incompressible and laminar within the range of studied Reynolds numbers (Re = 380, 760, 1140). In the present work, the average and local Nusselt number and pressure drop are investigated for Rectangular vortex generator (RVG) with varying angle of attack and attach angle. The numerical results indicate that the heat transfer and pressure drop increases with increasing the angle of attack to a certain range and then decreases with increasing angle of attack. Moreover, the attach angle also plays an importance role;a 90 attach angle is not necessary for enhancing the heat transfer. Usually, heat transfer enhancement is achieved at the expense of pressure drop penalty. To find the optimal position of vortex generator to obtain maximum heat transfer and minimum pressure drop, the data obtained from numerical simulations are used to train a BRANN (Bayesian-regularized artificial neural network). This in turn is used to drive multi-objective genetic algorithm (MOGA) to find the optimal parameters of VGs in the form of Pareto front. The optimal values of these parameters are finally presented.

关键词： Heat transfer enhancement longitudinal vortex generator flat-plate channel Artificial neural network multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Grid Connected Hybrid Energy System for Egyptian Residential Area

Optimal Grid Connected Hybrid Energy System for Egyptian Res...

引用

International Conference on Sustainable Energy Engineering and Application (ICSEEA) - Continuous Improvement of Sustainable Energy for Eco-Mobility

作者： Abdelshafy, Alaaeldin M. Hassan, Hamdy Mohamed, Abdelfatah M. El-Saady, G. Ookawara, Shinichi E JUST Alexandria Egypt Assiut Univ Fac Engn Assiut Egypt Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781538617656

This article presents a grid-connected hybrid energy system (HES) including a photovoltaic array (PV), wind turbines (WT), fuel cell (FC), electrolyzer, diesel generator, hydrogen tanks (HT) and battery systems. A multi-objective genetic algorithm (MOGA) is applied to find the optimal size of HES components with zero load rejection and selling the excess energy to the grid. The main aim of this article is to minimize the cost of electricity generated (COE) and CO2 emission simultaneously. A case study proposed hybrid energy system loads are some residential buildings (one block) in New Assiut City, Egypt with a peak value of 160.2 kW and yearly energy of 430 MWh. An optimal planning is obtained with a lifetime of 20 years. A comparison between different configurations of HES is done. Results showed that HES comprising PV/WT/battery/diesel is the most feasible economically and produce less CO2 emission than other configurations. Further, it is helping to decrease the load demand of the utility grid.

关键词： Photovoltaic array Wind turbine Battery system fuel cell electrolyzer multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

ARTIFICIAL NEURAL NETWORK MODELS: DATA SELECTION AND ONLINE ADAPTATION

ARTIFICIAL NEURAL NETWORK MODELS: DATA SELECTION AND ONLINE ...

引用

作者： HAMID REZA KHOSRAVANI UNIVERSIDADE DO ALGARVE

学位级别：博士

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings have the biggest proportion in energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. Hence this PhD was intended towards managing the energy consumed by Heating, Ventilating and Air Conditioning (HVAC) systems in buildings benefiting from Model Predictive Control (MPC) technique. To achieve this goal, artificial intelligence models such as neural networks and Support Vector Machines (SVM) have been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not noise-free. In this PhD, Radial Basis Function Neural Networks (RBFNN) as a promising class of Artificial Neural Networks (ANN) were considered to model a sequence of time series processes where the RBFNN models were built using multi objective genetic algorithm (MOGA) as a design platform. Regarding the design of such models, two main challenges were tackled; data selection and model adaptation. Since RBFNNs are data driven models, the performance of such models relies, to a good extent, on selecting proper data throughout the design phase, covering the whole input-output range in which they will be employed. The convex hull algorithms can be applied as methods for data selection; however the use of conventional implementations of these methods in high dimensions, due to their high complexity, is not feasible. As the first phase of this PhD, a new randomized approximation convex hull algorithm called ApproxHull was proposed for high dimensions so that it can be used in an acceptable execution time, and with low memory requirements. Simulation results showed that applying ApproxHull as a filter data selection method (i. e., unsupervised data selection method) could improve the performance of the class

关键词： Neural Networks multi-objective genetic algorithm Data Selection Online Adaptation

来源：评论

学校读者我要写书评

暂无评论

On Performance Improvement Based on Restart Meta-Heuristic Implementation for Solving multi-objective Optimization Problems 8th

On Performance Improvement Based on Restart Meta-Heuristic I...

引用

8th International Conference on Swarm Intelligence (ICSI)

作者： Brester, Christina Ryzhikov, Ivan Semenkin, Eugene Siberian State Aerosp Univ Krasnoyarsk Russia

ISBN: (纸本)9783319618333;9783319618326

One of the possible goals of multi-objective optimization is finding a set of non-dominated solutions or, in other words, a Pareto set approximation. Population-based algorithms, in particular, genetic algorithms, are widely used for this purpose because they deal with a set of alternative solutions, which might be helpful when a number of trade-off points should be obtained. To get a representative approximation, various regions of a search space should be explored. However, during the algorithm execution a search might be stuck in some areas. Therefore, in this article we present a new restart operator for multi-objective genetic algorithms which can prevent a search from stagnating, help to explore new regions and, as a result, improve the algorithm performance significantly. In our proposal we answer the two crucial questions of a restarting concept which are when to restart an algorithm and how to use previously found solutions. We introduce the algorithm independent restart operator, even though in this work we investigate it in combination with a certain MOGA. The experimental results prove the high effectiveness of the modified MOGA with the incorporated restart operator in comparison with the conventional one.

关键词： multi-objective genetic algorithm Restart operator Performance Benchmark problems

来源：评论

学校读者我要写书评

暂无评论

Solving and optimizing a bi-objective open shop scheduling problem by a modified genetic algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第5-8期85卷 1603-1613页

作者： Azadeh, Ali Goldansaz, SeyedMorteza Zahedi-Anaraki, AmirHossein Univ Tehran Sch Ind & Syst Engn Ctr Excellence Intelligent Based Expt Mech Coll EngnDept Engn Optimizat Res Tehran Iran

This paper presents a new bi-objective mixed-integer linear programming model for open shop scheduling problem. The main constraints of this model are considering independent setup times and sequence-dependent transportation times. Two objectives are presented, minimizing sum of the makespan for all jobs and minimizing sum of the machines' workload. Because of the complexity of this problem, it is known as a NP-hard problem;so, it cannot obtain an optimal solution in a reasonable time by using traditional methods. According to classic approach, some small- and medium- to large-sized instances are generated. For the small-sized ones, the Pareto-optimal solutions are produced exactly, and for the medium- to large-sized instances, we proposed a non-dominated sorting genetic algorithm (NSGA-II). To evaluate the performance of the proposed NSGA-II, the augmented epsilon-constraint method is used for the small-sized instances, and three performance metrics are proposed for the medium- to large-sized instances.

关键词： Bi-objective open shop scheduling Makespan Machine workload multi-objective genetic algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Combinations of Non-Sequential Regressors for ARX-Based Typhoon Inundation Forecast Models Considering multiple objectives

引用

WATER 2017年第7期9卷 519-519页

作者： Ouyang, Huei-Tau Shih, Shang-Shu Wu, Ching-Sen Natl Ilan Univ Dept Civil Engn Yilan 26047 Taiwan Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan

Inundation forecast models with non-sequential regressors are advantageous in efficiency due to their rather fewer input variables required to be processed. This type of model is nevertheless rare mainly because of the difficulty in finding the proper combination of regressors for the model to perform accurate prediction. A novel methodology is proposed in this study to tackle the problem. The approach involves integrating a multi-objective genetic algorithm (MOGA) with forecast models based on ARX (Auto-Regressive model with eXogenous inputs) to transfer the search for the optimal combination of non-sequential regressors into an optimization problem. An innovative approach to codifying any combinations of model regressors into binary strings is developed and employed in MOGA. The Pareto optimal sets of three types of models including linear ARX (LARX), nonlinear ARX with Wavelet function (NLARX-W), and nonlinear ARX with Sigmoid function (NLARX-S) are searched for by the proposed methodology. The results show that the optimal models acquired through this approach have good inundation forecasting capabilities in every aspect in terms of accuracy, time shift error, and error distribution.

关键词： typhoon inundation ARX model non-sequential regressors multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Power Efficient Resource Allocation in Cloud Computing Data Centers using multi-objective genetic algorithms and Simulated Annealing 4

Power Efficient Resource Allocation in Cloud Computing Data ...

引用

4th IEEE International Conference on Cloud Networking (CloudNet)

作者： Portaluri, Giuseppe Giordano, Stefano Univ Pisa I-56100 Pisa Italy

ISBN: (纸本)9781467395014

In this paper we present a new task allocator for Cloud Data Center (DC). The implementation is based on two different heuristics: multi-objective genetic algorithms (MOGA) and Simulated Annealing (SA). The allocator reduces at the same time both task completion time and server and switches power consumption, avoiding network link congestion. The evaluation results show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers with a quadratic time complexity for MOGA and a linear time complexity for SA.

关键词： Data center Cloud Computing multi-objective genetic algorithm Simulated Annealing Resource Allocation Power Efficiency

来源：评论

学校读者我要写书评

暂无评论

Capacity planning in thin film transistor - Liquid crystal display cell process

引用

JOURNAL OF MANUFACTURING SYSTEMS 2016年 39卷 63-78页

作者： Chen, James C. Chen, Tzu-Li Pratama, Bayu Rezki Tu, Qian-Fang Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Ind Management New Taipei Taiwan

The cell process in a thin film transistor-liquid crystal display (TFT-LCD) manufacturing is characterized as a complicated multi-stage and parallel-machine production system. This system has multiple products, sequence-dependent setup times, multi-step production, multi-objective, and matching constraints between color filter lines and array lines. This complex and capital-intensive production environment needs an efficient capacity planning decision that determines the order release schedule, estimated machine start and finished time of each order, and machine allocations to improve the performance of the entire production process. This research first proposes a finite capacity planning system (FCPS) to cope with the capacity planning problem faced by the TFT-LCD cell process based on the pull philosophy and assumption of finite capacity. A modified multi-objective genetic algorithm (MOGA) embedded in FCPS is also developed to efficiently generate the order release decision, which simultaneously minimizes the machine workload balance, makespan, and lateness. The preliminary experiment identifies the fine MOGA tuning parameters using a two-level factorial experimental design. Finally, the full factorial experiment evaluates the significance and robustness of the proposed modified MOGA algorithm compared with other competitive algorithms under various circumstances. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Capacity planning TFT-LCD Cell process Finite capacity multi-objective genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Interior High Frequency Noise Analysis of Heavy Vehicle Cab and multi-objective Optimization with Statistical Energy Analysis Method

引用

FLUCTUATION AND NOISE LETTERS 2017年第2期16卷 1750017-1750017页

作者： Chen, Shuming Wang, Lianhui Song, Jiqang Wang, Dengfeng Chen, Jing Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China FAW CAR CO LTD Changchun 130011 Peoples R China

The interior sound pressure levels of a commercial vehicle cab at the driver's right ear position and head rest position are determined as evaluation indices of vehicle acoustic performances. A statistical energy analysis model of the commercial vehicle cab was created by using statistical energy analysis method. The simulated interior acoustic performance of the cab has a significant coincidence with the experimental results. A response surface model was presented to determine the relationship between sound package parameters and evaluation indices of the interior acoustic performance for the vehicle cab. A multi-objective optimization was performed by using NSGA II algorithm with weighting coefficient method. The presented method provides a new idea for the multi-objective optimization design of the acoustic performances in vehicle noise analysis and control field.

关键词： Statistical energy analysis vehicle cab high frequency noise response surface method multi-objective genetic 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：