检索结果-内蒙古大学图书馆

Machine scheduling in custom furniture industry through neuro-evolutionary hybridization

APPLIED SOFT COMPUTING 2011年第2期11卷 1600-1613页

作者： Vidal, Juan C. Mucientes, Manuel Bugarin, Alberto Lama, Manuel Univ Santiago de Compostela Dept Elect & Comp Sci Santiago De Compostela 15782 Spain

Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron neural networks. (C) 2010 Elsevier B. V. All rights reserved.

关键词： Machine scheduling optimization multi-objective evolutionary algorithms NSGA-II Neural networks Hybrid approach

来源：评论

学校读者我要写书评

暂无评论

Robust multi-fidelity design of a micro re-entry unmanned space vehicle

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2011年第G11期225卷 1195-1209页

作者： Minisci, E. Vasile, M. Liqiang, H. Univ Strathclyde Dept Mech Engn Glasgow G1 1XJ Lanark Scotland Univ Glasgow Sch Engn Glasgow Lanark Scotland Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian Shaanxi Peoples R China

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preli

关键词： multi-objective evolutionary algorithms robust multi-disciplinary design meta-modelling optimal control unmanned space vehicles

来源：评论

学校读者我要写书评

暂无评论

Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions

引用

NATURAL COMPUTING 2011年第1期10卷 551-581页

作者： Vieira, Fabio R. J. Barbosa, Valmir C. Univ Fed Rio de Janeiro Programa Engn Sistemas & Computacao COPPE BR-21941972 Rio De Janeiro Brazil

The field of algorithmic self-assembly is concerned with the design and analysis of self-assembly systems from a computational perspective, that is, from the perspective of mathematical problems whose study may give insight into the natural processes through which elementary objects self-assemble into more complex ones. One of the main problems of algorithmic self-assembly is the minimum tile set problem, which in the extended formulation we consider, here referred to as MTSP, asks for a collection of types of elementary objects (called tiles) to be found for the self-assembly of an object having a pre-established shape. Such a collection is to be as concise as possible, thus minimizing supply diversity, while satisfying a set of stringent constraints having to do with important properties of the self-assembly process from its tile types. We present a study of what, to the best of our knowledge, is the first practical approach to MTSP. Our study starts with the introduction of an evolutionary heuristic to tackle MTSP and includes selected results from extensive experimentation with the heuristic on the self-assembly of simple objects in two and three dimensions, including the possibility of tile rotation. The heuristic we introduce combines classic elements from the field of evolutionary computation with a problem-specific variant of Pareto dominance into a multi-objective approach to MTSP.

关键词： Algorithmic self-assembly Minimum tile set problem multi-objective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

OPTIMAL BUFFER ALLOCATION FOR SEMI-SYNCHRONIZED AUTOMOTIVE ASSEMBLY LINES USING SIMULATION-BASED multi-objective OPTIMIZATION

OPTIMAL BUFFER ALLOCATION FOR SEMI-SYNCHRONIZED AUTOMOTIVE A...

引用

9th International Industrial Simulation Conference

作者： Bernedixen, Jacob Ng, Amos H. C. Univ Skovde Virtual Syst Res Ctr SE-54128 Skovde Sweden

ISBN: (纸本)9789077381632

A practical question in industry in designing or re-designing a production system is: how small can intermediate buffers be to ensure the desired production rate? This topic is usually called optimal buffer allocation as the goal is to allocate the minimum buffer capacities to optimize the performance of the line. This paper presents a case study of using simulation based evolutionary multi-objective optimization to determine the optimal buffer capacities and positions in the re-configuration of a real-world truck axle assembly line in an automobile manufacturer. The case study has not only revealed the applicability of the methodology in seeking optimal configurations in a truly multi-objective context, it also illustrates how additional important knowledge was gained by analyzing the optimization results in the objective space.

关键词： Synchronized Assembly Optimal Buffer Allocation Simulation-based Optimization multi-objective evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Coevolving Collection Plans for UAS Constellations 11

Coevolving Collection Plans for UAS Constellations

引用

13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Stouch, Daniel W. Zeidman, Ernest Richards, Marc McGraw, Kirk D. Callahan, William Charles River Analyt 625 Mt Auburn St Cambridge MA 02138 USA US Army ERDC CERL Cambridge MA 02138 USA

ISBN: (纸本)9781450305570

Our SPARTEN (Spatially Produced Airspace Routes from Tactical Evolved Networks) tool generates coordinated mission plans for constellations of unmanned aerial vehicles by allowing the mission planner to specify the importance of each objective for each mission. Using an evolutionary algorithm-based, multi-objective optimization technique, we consider factors such as area of analysis coverage, restricted operating zones, maximum ground control station range, adverse weather effects, military terrain value, airspace collision avoidance, path linearity, named area of analysis emphasis, and sensor performance. By employing novel visualizations using geographic information systems to represent their effectiveness, we help the user "look under the hood" of the algorithms and understand the viability and effectiveness of the mission plans to identify coverage gaps and other inefficiencies. In this paper, we apply multi-objective evolutionary algorithms to the air mission planning domain, with a focus on the visualization components.

关键词： evolutionary algorithms multi-objective evolutionary algorithms air operations mission planning unmanned systems

来源：评论

学校读者我要写书评

暂无评论

Robust Design of a Re-entry Unmanned Space Vehicle by multi-fidelity Evolution Control 11

Robust Design of a Re-entry Unmanned Space Vehicle by Multi-...

引用

13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Minisci, Edmondo Vasile, Massimiliano Univ Glasgow Sch Engn James Watt South Bldg Glasgow G12 8QQ Lanark Scotland Univ Strathclyde Dept Mech Engn Glasgow G1 1XJ Lanark Scotland

ISBN: (纸本)9781450305570

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimisation technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that minimise the mean value of the maximum heat flux, the mean value of the maximum achievable distance, and the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimisation process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach.

关键词： multi-objective evolutionary algorithms Robust multi Disciplinary Design Meta-modelling Optimal Control Unmanned Space Vehicle

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials

Multi-objective evolutionary algorithms and rough sets for d...

引用

IEEE International Conference on Granular Computing

作者： Smolinski, Tomasz G. Milanova, Mariofanna Boratyn, Grzegorz M. Buchanan, Roger Prinz, Astrid A. Emory Univ Atlanta GA 30322 USA Univ Arkansas Fayetteville AR 72701 USA Univ Louisville Louisville KY 40292 USA Arkansas State Univ State Univ AR 72467 USA

ISBN: (纸本)142440133X

Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (ie., sources of activity). Applied to measurements of brain activity in a controlled setting as well as under exposure to an external stimulus, they allow for analysis of the impact of the stimulus on those structures. The link between the stimulus and a given source can he confirmed by a classifier that is able to "predict" if a given signal was registered under one or the other condition, solely based on the components. Very often, however, statistical criteria used in traditional decomposition techniques turn out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel hybrid technique based on multi-objective evolutionary algorithms (MOEA) and rough sets (RS) that will perform decomposition in the light of the classification problem itself.

关键词： signal decomposition classification multi-objective evolutionary algorithms rough sets

来源：评论

学校读者我要写书评

暂无评论

Single and multi-objective evolutionary algorithms for the coordination of serial manufacturing operations

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2006年第2期17卷 251-270页

作者： Naso, D Turchiano, B Meloni, C Politecn Bari Dipartimento Elettrotecn & Elettron Bari Italy

This paper focuses on a typical problem arising in serial production, where two consecutive departments must sequence their internal work, each taking into account the requirements of the other one. Even if the considered problem is inherently multi-objective, to date the only heuristic approaches dealing with this problem use single-objective formulations, and also require specific assumptions on the objective function, leaving the most general case of the problem open for innovative approaches. In this paper, we develop and compare three evolutionary algorithms for dealing with such a type of combinatorial problems. Two algorithms are designed to perform directed search by aggregating the objectives of each department in a single fitness, while a third one is designed to search for the Pareto front of non-dominated solutions. We apply the three algorithms to considerably complex case studies derived from industrial production of furniture. Firstly, we validate the effectiveness of the proposed genetic algorithms considering a simple case study for which information about the optimal solution is available. Then, we focus on more complex case studies, for which no a priori indication on the optimal solutions is available, and perform an extensive comparison of the various approaches. All the considered algorithms are able to find satisfactory solutions on large production sequences with nearly 300 jobs in acceptable computation times, but they also exhibit some complementary characteristics that suggest hybrid combinations of the various methods.

关键词： multi-objective evolutionary algorithms scheduling sequencing manufacturing systems

来源：评论

学校读者我要写书评

暂无评论

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

引用

INFORMATION SCIENCES 2010年第10期180卷 1924-1941页

作者： Marcelloni, Francesco Vecchio, Massimo Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy INRIA Saclay Ile De France Sud France

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Wireless sensor networks Data compression multi-objective evolutionary algorithms Energy efficiency Signal processing

来源：评论

学校读者我要写书评

暂无评论

A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm

引用

APPLIED SOFT COMPUTING 2010年第2期10卷 548-561页

作者： Lazzerini, Beatrice Marcelloni, Francesco Vecchio, Massimo INRIA ASAP Res Team F-91893 Orsay France Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

The JPEG algorithm is one of the most used tools for compressing images. The main factor affecting the performance of the JPEG compression is the quantization process, which exploits the values contained in two tables, called quantization tables. The compression ratio and the quality of the decoded images are determined by these values. Thus, the correct choice of the quantization tables is crucial to the performance of the JPEG algorithm. In this paper, a two-objective evolutionary algorithm is applied to generate a family of optimal quantization tables which produce different trade-offs between image compression and quality. Compression is measured in terms of difference in percentage between the sizes of the original and compressed images, whereas quality is computed as mean squared error between the reconstructed and the original images. We discuss the application of the proposed approach to well-known benchmark images and show how the quantization tables determined by our method improve the performance of the JPEG algorithm with respect to the default tables suggested in Annex K of the JPEG standard. (C) 2009 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithms Image compression JPEG 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：