检索结果-内蒙古大学图书馆

Hypersonic Blunt-Body Shape Optimization for Reducing Drag and Heat Transfer

JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2017年第1期31卷 48-55页

作者： Seager, Christopher Agarwal, Ramesh K. Washington Univ Dept Mech Engn & Mat Sci St Louis MO 63130 USA Washington Univ Dept Mech Engn & Mat Sci Engn St Louis MO 63130 USA

A large design concern for high-speed vehicles such as next-generation launch vehicles or reusable spacecraft is the drag and heat transfer experienced at hypersonic velocities. In this paper, the optimized shapes for both minimum drag and minimum peak heat flux for an axisymmetric blunt body are developed using computational-fluid-dynamics software in conjunction with a genetic algorithm. For flowfield calculations, the commercial flow solver ANSYS Fluent is employed to solve the unsteady compressible Reynolds-averaged Navier-Stokes equations in conjunction with the shear-stress transport k-omega turbulence model. The hypersonic body shape is optimized using a multi-objective genetic algorithm to minimize both the drag and heat transfer. The multi-objective genetic algorithm creates a Pareto-optimal front containing the optimized shapes for various relative objectives of minimized drag and heat transfer. The results show a significant decrease in both the drag and peak heat flux and exhibit the expected changes in the body profile. It should be noted that shape optimizations of a blunt body in hypersonic flow for reducing both drag and heat flux through use of a multi-objective genetic algorithm are reported in this paper for the first time in the literature. The proposed methodology will allow the simulation and optimization of more complex shapes of hypersonic vehicles.

关键词： Heat transfer Genetic algorithm (GA) drag shape optimization multi-objective evolutionary algorithm minimum drag blunt bodies hypersonic vehicles hypersonic speed hypersonic flow CONJUNCTION Heat flux

来源：评论

学校读者我要写书评

暂无评论

Non-destructive inspection of carbon-fibre-reinforced polymers using lock-in thermography

引用

JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2013年第8期32卷 533-539页

作者： Wang, Zijun Dai, Jingmin Zhu, Zhaoxuan Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 610054 Peoples R China Harbin Inst Technol Dept Automat Measurement & Control Harbin Peoples R China

Development in composite materials technology has led to wide applications of such promising materials. The detection and characterisation of the wide range of defects requires a number of specialised non-destructive methods. In this article, lock-in thermography was used to identify defects in carbon-fibre-reinforced polymer specimens. The effects of modulation frequency and incident angle were discussed. The amplitude and the phase images were compared. A multi-objective evolutionary algorithm was used to optimise the experimental frequency, time and other parameters of infrared NDT. The temperature difference between the non-destructive and the defect regions was used as evaluation function. The results show this method can provide theoretical parameters for engineering inspection.

关键词： Lock-in thermography non-destructive carbon-fibre-reinforced polymers multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

A matheuristic for a bi-objective demand-side optimization for cooperative smart homes

引用

ELECTRICAL ENGINEERING 2020年第4期102卷 1913-1930页

作者： Garroussi, Zineb Ellaia, Rachid El-ghazali-Talbi Lucas, Jean-yves Mohammed V Univ Rabat Mohammadia Sch Engineers Engn Smart & Sustainable Syst Res Ctr E3S LERMA Lab Ibn Sina Ave 765 Rabat Morocco Inria Lille Nord Europe Res Ctr Big Optimizat & Ultra Scale Comp Team BONUS F-59655 Villeneuve Dascq France Elect France SA EDF R&D 1 Ave Gen de Gaulle F-92140 Clamart France

This paper proposes the demand-side management (C-DSM) of collaborative homes during one day. Some homes may have photovoltaic panels and batteries, other homes may have batteries, and the remaining homes are pure energy consumers. Homes are interconnected and connected to the main grid. The proposed C-DSM is formulated as a constrained bi-objective and mixed-integer linear optimization model with one objective related to the total net energy cost and the other related to discomfort caused by allowing flexibility of controllable loads within an acceptable comfort range. A matheuristic approach has been proposed to determine an efficient Pareto set for the problem, combining the non-dominated sorting genetic algorithm II (NSGAII) with an exact solver. In this approach, discrete decision variables are represented as partial chromosomes and an exact solver determines the continuous decision variables in an optimal way. A number of simulations are performed and compared with the weighted sum algorithm (WSA) under four cases for small to large number of homes. The results demonstrate the effectiveness of power cooperation among homes and show that our algorithm is able to obtain more Pareto solutions in a much shorter time that are far better than those obtained by the WSA. The proposed algorithm is suitable for large-sized C-DSM problem instances, promotes power cooperation between homes, reduces the dependency to the main grid and achieves individual fairness of energy net cost of each home without the need for installation of photovoltaic panels and batteries for all homes.

关键词： Microgrids Demand-side management of multiple homes multi-objective evolutionary algorithm Mixed-integer linear programming Matheuristic

来源：评论

学校读者我要写书评

暂无评论

multi-objective search of robust neural architectures against multiple types of adversarial attacks

引用

NEUROCOMPUTING 2021年 453卷 73-84页

作者： Liu, Jia Jin, Yaochu Univ Surrey Dept Comp Sci Surrey GU2 7XH Surrey England

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design network architectures that are robust to one particular type of adversarial attacks. It is practically impossible, however, to predict beforehand which type of attacks a machine learn model may suffer from. To address this challenge, we propose to search for deep neural architectures that are robust to five types of well-known adversarial attacks using a multi-objective evolutionary algorithm. To reduce the computational cost, a normalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation. All non-dominated network architectures obtained by the proposed method are then fully trained against randomly chosen adversarial attacks and tested on two widely used datasets. Our experimental results demonstrate the superiority of optimized neural architectures found by the proposed approach over state-of-the-art networks that are widely used in the literature in terms of the classification accuracy under different adversarial attacks. (c) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Adversarial attacks Neural architecture search Robustness

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolution strategy for multimodal multi-objective optimization

引用

APPLIED SOFT COMPUTING 2021年 101卷 107004-107004页

作者： Zhang, Kai Chen, Minshi Xu, Xin Yen, Gary G. Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objective optimization problems. However, existing MOEAs cannot satisfactorily address multimodal multi-objective optimization problems that demand to find multiple groups of optimal solutions simultaneously. In this paper, we propose an evolution strategy to solve multimodal multi-objective optimization problems, named MMO-MOES. This paper focus on searching for well-converged and well-distributed solutions in the decision space. Firstly, a novel niching strategy in the decision space, which imitates the repulsive force among isotropic magnetic particles, is adopted to drive the individuals to preserve uniform distances from each other and spread to the whole Pareto set automatically. This strategy is effective in finding multiple groups of optimal solutions simultaneously. Secondly, MMO-MOES requires only a very small population size to obtain a well-distributed and well-converged set of Pareto optimal solutions in the decision space. The greater the population size, the clearer contour of the approximate Pareto sets and Pareto front will be. Finally, the MMO-MOES is compared against some chosen leading-edge MMOEAs. The experimental results demonstrate that MMO-MOES provides exceptional performance in searching for the complete Pareto subsets and Pareto front on Omni-test problem, Symmetrical Parts (SYM-PART) problems, and CEC 2019 multimodal multi-objective Optimization Problems (MMOPs) test suite. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm multimodal multi-objective optimization problem Evolution strategy

来源：评论

学校读者我要写书评

暂无评论

Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

引用

SWARM AND evolutionary COMPUTATION 2023年 77卷

作者： An, Youjun Chen, Xiaohui Gao, Kaizhou Zhang, Lin Li, Yinghe Zhao, Ziye Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Collaborat Lab Intelligent Sci & Syst Macau 999078 Peoples R China

Nowadays, the make-to-order (MTO) production has gradually become a new manufacturing trend, and the real-time arrival of orders has brought great challenges to order acceptance, production scheduling and maintenance planning in the actual production. Therefore, in this study, we focus on the integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints. More precisely, (1) a multi-level imperfect maintenance model with minimal repair (MR), preventive maintenance (PM), overhaul maintenance (OM) and replacement is designed for each production machine, and the optimality is derived;(2) an integrated multi-objective optimization model is developed to characterize the concerned problem;and (3) an improved non-dominated sorting genetic algorithm III with adaptive reference vector (INSGA-III/ARV) is proposed to deal with the problem. In the numerical simulation, the effect of different order sorting rules is first analyzed. Second, the effectiveness of improved operators is demonstrated by internal analysis of the proposed algorithm. Third, the superiority and competitiveness of the proposed algorithm is verified by comparing with the variants of five state-of-the-art algorithms. Fourth, the benefits of both the designed multi-level imperfect maintenance model and real-time order acceptance and scheduling (ROAS) strategy are proved by contrasting with other two traditional maintenance models and all order acceptance and scheduling (AOAS) strategy, respectively. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several key parameters on the integrated optimization model.

关键词： Real-time order acceptance Flexible job-shop rescheduling multi-level imperfect maintenance multi-objective evolutionary algorithm (MOEA)

来源：评论

学校读者我要写书评

暂无评论

A direct solution framework for structural optimization problems with interval uncertainties

引用

APPLIED MATHEMATICAL MODELLING 2020年 80卷 384-393页

作者： Fu, Chunming Liu, Zhiwen Deng, Jian Univ South China Coll Mech Engn Hengyang 421001 Peoples R China Univ South China Cooperat Innovat Ctr Nucl Fuel Cycle Technol & Eq Hengyang 421001 Peoples R China

A direct solution framework based on multi-objective evolutionary algorithm is developed to solve the structural optimization problems with interval uncertainties. The midpoint and radius of the uncertain original objective are treated as two equally important objectives, which are solved by a multi-objective evolutionary algorithm. The satisfaction value of interval possibility degree model is utilized to deal with nonlinear uncertain constraints and then the degree of constraint violation based on this model is calculated to judge the design vector individuals which one is feasible or infeasible. Subsequently, a selection strategy based on interval constrained-domination rule is utilized to realize the ranking of different design vectors. Finally, two numerical examples and the structural design of augmented reality glasses are investigated to verify the applicability and effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Interval optimization multi-objective evolutionary algorithm Uncertain structural design Constrained-domination

来源：评论

学校读者我要写书评

暂无评论

Characterization of graph properties for improved Pareto fronts using heuristics and EA for bi-objective graph coloring problem

引用

APPLIED SOFT COMPUTING 2013年第5期13卷 2812-2822页

作者： Saha, Soma Kumar, Rajeev Baboo, Gyan Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 WB India

Bi-objective graph coloring problem (BOGCP) is a generalized version in which the number of colors used to color the vertices of a graph and the corresponding penalty which incurs due to coloring the endpoints of an edge with the same color are simultaneously minimized. In this paper, we have analyzed the graph density, the interconnection between high degree nodes of a graph, the rank exponent of the standard benchmark input graph instances and observed that the characterization of graph instances affects on the behavioral quality of the solution sets generated by existing heuristics across the entire range of the obtained Pareto fronts. We have used multi-objective evolutionary algorithm (MOEA) to obtain improved quality solution sets with the problem specific knowledge as well as with the embedded heuristics knowledge. To establish this fact for BOGCP, hybridization approach is used to construct recombination operators and mutation operators and it is observed from empirical results that the embedded problem specific knowledge in evolutionary operators helps to improve the quality of solution sets across the entire Pareto front;the nature of problem specific knowledge differentiates the quality of solution sets. (C) 2012 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms multi-objective optimization Hybridization Graph coloring problem Graph density Pareto front Rank histogram multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Parallelization of a non-linear multi-objective optimization algorithm: Application to a location problem

引用

APPLIED MATHEMATICS AND COMPUTATION 2015年 255卷 114-124页

作者： Gila Arrondo, Aranzazu Redondo, Juana L. Fernandez, Jose Ortigosa, Pilar M. Univ Murcia Dept Stat & Operat Res Murcia Spain Univ Granada Dept Comp Architecture & Technol CeiA3 Agrifood Campus Int Excellence Granada Spain Univ Almeria CeiA3 Dept Informat Agrifood Campus Int Excellence Almeria Spain

Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly intractable. In order to cope with those problems, several (meta) heuristic procedures have been developed during the last decade whose aim is to obtain a good discrete approximation of the Pareto-front. In this vein, a new multi-objective evolutionary algorithm, called FEMOEA, which can be applied to many nonlinear multi-objective optimization problems, has recently been proposed. Through a comparison with an exact interval branch-and-bound algorithm, it has been shown that FEMOEA provides very good approximations of the Pareto-front. Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D. Comprehensive computational studies have shown that, among the studied algorithms, FEMOEA was the one providing, on average, the best results for all the quality indicators analyzed. However, when the set approximating the Pareto-front must have many points (because a high precision is required), the computational time needed by FEMOEA may not be negligible at all. Furthermore, the memory requirements needed by the algorithm when solving those instances may be so high that the available memory may not be enough. In those cases, parallelizing the algorithm and running it in a parallel architecture may be the best way forward. In this work, a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. The results show that FEMOEA-Paral is able to maintain the effectiveness of the sequential version and this by reducing the computational costs. Furthermore, the parallel version shows good scalability. The efficiency results have been analyzed by means of a profiling and tracing toolkit for performance analysis. (C) 2014 Elsevier Inc. All

关键词： Nonlinear multi-objective optimization multi-objective evolutionary algorithm Parallelism Efficiency Effectiveness Tuning and Analysis Utilities Performance System Facility location problem

来源：评论

学校读者我要写书评

暂无评论

Bi-objective green in-house transportation scheduling and fleet size determination in mixed-model assembly lines with mobile robots

引用

ENGINEERING COMPUTATIONS 2022年第7期39卷 2630-2654页

作者： Zhou, Binghai Zhang, Jihua Fei, Qianran Tongji Univ Inst Ind Engn Sch Mech Engn Shanghai Peoples R China

Purpose Facing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well. Design/methodology/approach This research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a multi-objective Discrete Differential Evolution algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem. Findings The performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem. Originality/value This study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

关键词： Mixed-model assembly line Energy efficiency Optimization multi-objective evolutionary algorithm In-house transportation

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：