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作者： Zhang, Xiaojia Shelly Georgia Institute of Technology

学位级别：博士

Topology optimization is a practical tool that allows for improved structural designs. This thesis focuses on developing both theoretical foundations and computational algorithms for topology optimization to effectively and efficiently handle many materials, many constraints, and many load cases. Most work in topology optimization is restricted to linear material with limited constraint settings for multiple materials. To address these issues, we propose a general multi-material topology optimization formulation with material nonlinearity. This formulation handles an arbitrary number of materials with flexible properties, features freely specified material layers, and includes a generalized volume constraint setting. To efficiently handle such arbitrary constraints, we derive an update scheme, named ZPR, that performs robust updates of design variables associated with each constraint independently. The derivation is based on the separable feature of the dual problem of the convex approximated primal subproblem with respect to the Lagrange multipliers, and thus the update of design variables in each constraint only depends on the corresponding Lagrange multiplier. This thesis also presents an efficient filtering scheme, with reduced-order modeling, and demonstrates its application to 2D and 3D topology optimization of truss networks. The proposed filtering scheme extracts valid structures, yields the displacement field without artificial stiffness, and improve convergence, leading to drastically improved computational performance. To obtain designs under many load cases, we present a randomized approach that efficiently optimizes structures under hundreds of load cases. This approach only uses 5 or 6 stochastic sample load cases, instead of hundreds, to obtain similar optimized designs (for both continuum and truss approaches). Through examples using Ogden-based, bilinear, and linear materials, we demonstrate that proposed topology optimization frameworks with the ne

关键词： Topology optimization multi-material topology optimization Nonlinear topology optimization multi-objective optimization Density-based method Ground structure method Truss layout optimization Michell structures Optimality conditions Material nonlinearity Hyperelastic model multiple constraints multiple load cases Discrete filter Material selection algorithm Update scheme ZPR update algorithm Stochastic sampling Randomized algorithm Additive manufacturing

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A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

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Science China(Information Sciences) 2017年第11期60卷 198-210页

作者： Xiangmin GUAN Xuejun ZHANG Renli LV Jun CHEN Weiszer MICHAL Department of General Aviation Civil Aviation Management Institute of China School of Electronic and Information Engineering Beihang University School of Engineering University of Lincoln

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide controllers with variety of optional solutions, representing different tradeoffs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem, which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route network needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence,in order to further improve the search capability of the solution algorithm, a cooperative co-evolution(CC)algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition(MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm(MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view.

关键词： air traffic management conflict avoidance combinatorial optimization multi-objective coopera tive co-evolution

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An algorithm for multidimensional optimization and robustness evaluation within a rescheduling procedure

An algorithm for multidimensional optimization and robustnes...

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2018 World Congress on Engineering, WCE 2018

作者： Brignolo, Leonardo Fabig, Christian Revetria, Roberto 16145 Italy Institute of Material Handling and Industrial Engineering Technische Universität Dresden Dresden01062 Germany

ISBN: (纸本)9789881404893

Project schedules supporting the production and maintenance of heavy special machinery, ships and aircrafts have to be updated, e.g. due to changed order contents and dates, resource breakdowns, further projects etc. In addition to the production target, an update should also aim to have as few deviations as possible to a current schedule (i.e. solution robustness), aiming to prevent revisions of further decisions (e.g. material procurement, subcontracting, worker attendance). This raises the need for a multi-objective evaluation of deviations and a proper rescheduling procedure. The aim of the paper is to design two algorithms for optimization and robustness evaluation, perceiving robustness as deviations between one current schedule and one possible reschedule created by the simulation-based optimization. In contrast to existing approaches, we propose to measure those deviations with respect to various dimensions. Two algorithms are designed, measuring timely deviations (e.g. deviation of milestones) and those of resources (e.g. short term change of shifts). To facilitate practical implications, a case study from aircraft maintenance is used. Results reveal that the multidimensional evaluation of deviations serves as a valuable extension of optimization tools in order to gather practical preferences and limitations within a heuristic rescheduling procedure. © 2018 Newswood Limited. . All rights reserved.

关键词： Machinery

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Study of route optimization considering bottlenecks and fairness among partial paths 10

Study of route optimization considering bottlenecks and fair...

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10th International Conference on Agents and Artificial Intelligence, ICAART 2018

作者： Matsui, Toshihiro Silaghi, Marius Hirayama, Katsutoshi Yokoo, Makoto Matsuo, Hiroshi Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan Florida Institute of Technology MelbourneFL32901 United States Kobe University 5-1-1 Fukaeminami-machi Higashinada-ku Kobe658-0022 Japan Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

ISBN: (纸本)9789897582752

Route optimization is an important problem for single agents and multi-agent systems. In route optimization tasks, the considered challenges generally belong to the family of shortest path problems. Such problems are solved using optimization algorithms, such as the A∗algorithm, which is based on tree search and dynamic programming. In several practical cases, cost values should be as evenly minimized for individual parts of paths as possible. These situations are also considered as multi-objective problems for partial paths. Since dynamic programming approaches are employed for the shortest path problems, different types of criteria which can be decomposed with dynamic programming might be applied to the conventional solution methods. For this class of problems, we employ a leximax-based criterion, which considers the bottlenecks and unfairness among the cost values of partial paths. This criterion is based on a similar criterion called leximin for multiobjective maximization problems. It is also generalized for objective vectors which have variable lengths. We address an extension of the conventional A∗search algorithm and investigate an issue concerning on-line search algorithms. The influence of the proposed approach is experimentally evaluated. © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： Dynamic programming

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A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

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SCIENTIA IRANICA 2018年第3期25卷 1750-1767页

作者： Shishebori, D. Babadi, A. Yousefi Noormohammadzadeh, Z. Yazd Univ Dept Ind Engn POB *** Yazd Iran Univ Tehran Sch Ind & Syst Engn Tehran Iran

This study considers a multi-objective combined budget constrained Facility Location/Network Design Problem (FL/NDP), in which system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including transshipment costs, Facility Location (FL) costs, and fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider several system uncertainties, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. In the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering practical factors (e.g. several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and, subsequently, more efficient utilization of the established network. (C) 2018 Sharif University of Technology. All rights reserved.

关键词： Facility location Network design Robust optimization Mixed integer programming Fuzzy multi-objective Sub-gradient based Lagrangian relaxation

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multi-criteria algorithms for portfolio optimization under practical constraints

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 37卷 104-125页

作者： Meghwani, Suraj S. Thakur, Manoj Indian Inst Technol Mandi Suran Himachal Prades India

Selection of promising assets and allocating capital among them is a crucial part of the financial decision making process. Modern portfolio theory formulated it as a quadratic optimization problem of maximizing expected returns and minimizing the risk of the portfolio. This problem was modified to incorporate investor's preferences resulting in discrete non-linear search space which cannot be handled by traditional quadratic programming approaches. Relevant literature shows the success of evolutionary algorithms in modelling some of these preferences multi-criteria algorithms for portfolio optimization under practical constraintsin a constrained optimization problem. This study proposes a candidate generation procedure and a repair mechanism for practical portfolio optimization model in multi-objective evolutionary algorithm (MOEA) settings. Both these methods together can handle a larger class of constraints namely cardinality, pre assignment, budget, quantity (floor and ceiling) and round-lot constraints. Proposed methods can easily be incorporated into existing evolutionary algorithms. To evaluate their effectiveness, four MOEAs namely Non dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Global Weighting Achievement Scalarizing Function Genetic Algorithm (GWASFGA) and Pareto Envelope-based Selection Algorithm-II (PESA-II) have been adapted and their capability of approximating unconstrained efficient frontier are discussed. For empirical testing, seven datasets involving maximum up to 1290 assets are used. All 'the adapted algorithms are compared and evaluated on the basis of five well-known performance metrics for MORAs. The potential of our adapted algorithms is presented in comparison with existing MOEAs for the identical problems.

关键词： Portfolio optimization multi-objective optimization Cardinality constrained portfolio problem Repair mechanism

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multi-objective optimization of passive filters in industrial power systems

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ELECTRICAL ENGINEERING 2017年第1期99卷 387-395页

作者： Leite, Jandecy Cabral Perez Abril, Ignacio de Lima Tostes, Maria Emilia Limao de Oliveira, Roberto Celio Inst Technol & Educ Galileo Amazonia ITEGAM Praca Francisco Pereira da Silva 149 Manaus Amazonas Brazil Univ Cent Marta Abreu Las Villas Ctr Estudios Electroenerget Carretera Camajuani Km 5 Santa Clara Villa Clara Cuba Fed Univ Para ITEC UFPA Inst Technol Ave Augusto Correa 01 Belem Amazonas Brazil

The formulations employed to the optimization of passive filters can be classified as formulations of a single objective or formulations with multiple objectives. The single-objective formulations normally are devoted to determine the filters of lowest cost that assure the compliance with the power quality standards, while in the multi-objective approaches are added other goals that are related with the improvement of the power quality indexes. In the presented approach, the problem of the reactive power compensation and the problem of the harmonic distortion compensation are considered a unified multi-objective problem in which is determined a set of passive filters that allows the obtaining of the maximum economic benefits as well as the maximum improvement of the power quality of the circuit. While several previous contributions solve the multi-objective problem by minimizing a single function composed of several sub-objectives, this work employs the non-dominated sorting genetic algorithm (NSGA-II) for the optimization of three independent objective functions. This algorithm obtains the Pareto front of the problem and allows the selection of the most effective solutions. The optimization method that presented in this work allows the selection by the algorithm of the filter type proper for compensation in a node of the circuit as well as the obtaining of their parameters. The set of possible filter configurations to place in one candidate node is defined by the user before the optimization is done. This is a distinctive characteristic of the presented approach that is tested with a practical example.

关键词： Passive filters Distribution systems Harmonics NSGA-II

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EFA-BMFM: A multi-criteria framework for the fusion of colour image segmentation

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INFORMATION FUSION 2017年 38卷 104-121页

作者： Khelifi, Lazhar Mignotte, Max Univ Montreal DIRO Montreal PQ Canada

Considering the recent progress in the development of practical applications in the field of image processing, it is increasingly important to develop new, efficient and more reliable algorithms to solve an image segmentation problem. To this end, various fusion-based segmentation approaches which use consensus clustering, and which are based on the optimization of a single criterion, have been proposed. One of the greatest challenges with these approaches is to select the best fusion criterion, which gives the best performance for the image segmentation model. In this paper, we propose a new fusion model of image segmentation based on multi-objective optimization, which aims to overcome the limitation and bias caused by a single criterion, and to provide a final improved segmentation. To address the ill-posedness for the search of the best criterion, the proposed fusion model combines two conflicting and complementary criteria for segmentation fusion, namely, the region-based variation of information (Vol) criterion and the contour-based F-measure (precision-recall) criterion using an entropy-based confidence weighting factor. To optimize our energy-based model, we propose an extended local optimization procedure based on superpixels and derived from the iterative conditional mode (ICM) algorithm. This new multi objective median partition-based approach, which relies on the fusion of inaccurate, quick and spatial clustering results, has emerged as an appealing alternative to the use of traditional segmentation fusion models which exist in the literature. We perform experiments using the Berkeley database with manual ground truth segmentations, and the results clearly show the feasibility and efficiency of the proposed methodology. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Colour textured image segmentation Combination of multiple segmentations multi-objective optimization Berkeley image database Entropy

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A novel optimization framework for designing multi-stream compact heat exchangers and associated network

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APPLIED THERMAL ENGINEERING 2017年 116卷 110-125页

作者： Wang, Zhe Sunden, Bengt Li, Yanzhong Xi An Jiao Tong Univ Sch Energy & Power Engn Xian Peoples R China Lund Univ Dept Energy Sci Lund Sweden

Despite the performance gains of heat exchangers by thermal-hydraulic optimization, many design approaches suffer from a long process of trial-and-error, limited practical problem formulation and poor robustness. This paper addresses those challenges and develops an optimization framework for designing multi-stream compact heat exchangers. The optimization framework consists of a multi-stream matching organization design with Pinch analysis principles on the heat exchanger network, fin selection and relevant layer pattern using multi-objective algorithm for the thermal-hydraulic modeling, and a field synergy optimization analysis for the temperature field of exchanger. There are compatible with each other by the mutual restriction of design parameters and the process requirements. Finally, by means of a segmented differential calculation, the designed temperature field of all the streams and layers can be obtained. Design capabilities of this optimization framework are investigated on two multi stream heat exchangers for cryogenic air separation processes. The optimization results are compared with traditional design methods, proving that this framework not only satisfies the specific energy requirement, but also to some extent improves the heat transfer efficiency and reduces the fluid resistance. The developed optimization framework provides a flexible and robust method for numerical simulation and optimization design of multi-stream heat exchangers. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multi-stream compact heat exchangers Heat exchanger networks optimization framework Stream-matching organization Fin structure Layer pattern

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Design concepts of induction mass heating technology based on multiple-criteria optimization

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2017年第2期36卷 386-400页

作者： Pleshivtseva, Yuliya Rapoport, Edgar Nacke, Bernard Nikanorov, Alexander Di Barba, Paolo Forzan, Michele Lupi, Sergio Sieni, Elisabetta Samara State Tech Univ Dept Heat and Power Engn Samara Russia Samara State Tech Univ Dept Automat & Informat Technol Samara Russia Leibniz Univ Hannover Inst Electrotechnol Hannover Germany Univ Pavia Dept Elect Engn Pavia Italy Univ Padua Dept Ind Engn Padua Italy

Purpose - The purpose of this paper is to describe main ideas and demonstrate results of the research activities carried out by the authors in the field of design concepts of induction mass heating technology based on multiple-criteria optimization. The main goal of the studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the multi-objective optimization problem that is mathematically formulated in terms of the most important optimization criteria, for example, maximum temperature uniformity, maximum energy efficiency and minimum scale formation. Design/methodology/approach - Standard genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA) and alternance method of parametric optimization based on the optimal control theory are applied as effective tools for the practice-oriented problems for multiple-criteria optimization of induction heaters' design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into the automated optimization procedure. Findings - optimization procedures are tested and investigated for two-and three-criteria optimization problems solution on the examples of induction heating of a graphite disk, induction heating of aluminum and steel billets prior to hot forming. practical implications - Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design and control of different electromagnetic devices and systems. Originality/value - The paper describes main ideas and results of the research activities carried out by the authors during past years in the field of multiple-criteria optimization of induction heaters' design based on numerical coupled electromagnetic and temper

关键词： Induction heating Optimal design multi-objective optimisation

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