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multi-physics optimization of three-dimensional microvascular polymeric components

JOURNAL OF COMPUTATIONAL PHYSICS 2013年第1期233卷 132-147页

作者： Aragon, Alejandro M. Saksena, Rajat Kozola, Brian D. Geubelle, Philippe H. Christensen, Kenneth T. White, Scott R. Univ Illinois Civil & Environm Engn Dept Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA

This work discusses the computational design of microvascular polymeric materials, which aim at mimicking the behavior found in some living organisms that contain a vascular system. The optimization of the topology of the embedded three-dimensional microvascular network is carried out by coupling a multi-objective constrained genetic algorithm with a finite-element based physics solver, the latter validated through experiments. The optimization is carried out on multiple conflicting objective functions, namely the void volume fraction left by the network, the energy required to drive the fluid through the network and the maximum temperature when the material is subjected to thermal loads. The methodology presented in this work results in a viable alternative for the multi-physics optimization of these materials for active-cooling applications. (C) 2012 Elsevier Inc. All rights reserved.

关键词： multi-physics optimization Microvascular materials Active cooling multi-objective genetic algorithms Thermal management optimization

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multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2008年第3期31卷 243-264页

作者： Alhajj, Reda Kaya, Mehmet Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Firat Univ Dept Comp Engn TR-23169 Elazig Turkey Global Univ Dept Comp Sci Beirut Lebanon

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets;for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective genetic algorithms (GA);the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms;2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.'s work in terms of runtime, number of large itemsets and number of association rules.

关键词： Automated clustering CURE Data mining Fuzziness Fuzzy association rules multi-objective genetic algorithms

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Feature selection for face recognition based on multi-objective evolutionary wrappers

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第13期40卷 5077-5084页

作者： Vignolo, Leandro D. Milone, Diego H. Scharcanski, Jacob Univ Nacl Litoral CONICET Grp Invest Senales & Inteligencia Computac Dept InformatFac Ingn & Ciencias Hidr Buenos Aires DF Argentina Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Dept Engn Eletr BR-91501970 Porto Alegre RS Brazil

Feature selection is a key issue in pattern recognition, specially when prior knowledge of the most discriminant features is not available. Moreover, in order to perform the classification task with reduced complexity and acceptable performance, usually features that are irrelevant, redundant, or noisy are excluded from the problem representation. This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks. The proposed strategy explores the space of multiple feasible selections in order to minimize the cardinality of the feature subset, and at the same time to maximize its discriminative capacity. Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Wrappers multi-objective genetic algorithms Feature selection Face recognition

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Cost-aware Pareto Optimal Test Suite Minimisation for Service-centric Systems 13

Cost-aware Pareto Optimal Test Suite Minimisation for Servic...

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15th genetic and Evolutionary Computation Conference (GECCO)

作者： Bozkurt, Mustafa UCL CREST Dept Comp Sci London WC1E 6BT England

ISBN: (纸本)9781450319638

Runtime testing cost caused by service invocations is considered as one of the major limitations in Service-centric System Testing (ScST). Unfortunately, most of the existing work cannot achieve cost reduction at runtime as they perform offline testing. In this paper, we introduce a novel cost-aware pareto optimal test suite minimisation approach for ScST aimed at reducing runtime testing cost. In experimental analysis, the proposed approach achieved reductions between 69% and 98.6% in monetary cost of service invocations while retaining test suite coverage. The results also provided evidence for the effectiveness of the selected algorithm HNSGA-II over the two commonly used algorithms: Greedy and NSGA-II.

关键词： SOA web services service-centric systems testing multi-objective genetic algorithms test suite minimisation

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A Hybrid Soft Computing Approach for Optimizing Design Parameters of Electrical Drives

A Hybrid Soft Computing Approach for Optimizing Design Param...

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7th Int Conf on Soft Comp Models in Industrial and Environm Applications

作者： Zavoianu, Alexandru-Ciprian Bramerdorfer, Gerd Lughofer, Edwin Silber, Siegfried Amrhein, Wolfgang Klement, Erich Peter Johannes Kepler Univ Linz Dept Knowledge Based Math Syst Fuzzy Log Lab Linz Hagenberg Linz Austria Johannes Kepler Univ Linz Inst Elect Drives & Power Elect Linz Austria Austrian Ctr Competence Mech Linz Austria

ISBN: (纸本)9783642329210

In this paper, we are applying a hybrid soft computing approach for optimizing the performance of electrical drives where many degrees of freedom are allowed in the variation of design parameters. The hybrid nature of our approach originates from the application of multi-objective evolutionary algorithms (MOEAs) to solve the complex optimization problems combined with the integration of non-linear mappings between design and target parameters. These mappings are based on artificial neural networks (ANNs) and they are used for the fitness evaluation of individuals (design parameter vectors). The mappings substitute very time-intensive finite element simulations during a large part of the optimization run. Empirical results show that this approach finally reduces the computation time for single runs from a few days to several hours while achieving Pareto fronts with a similar high quality.

关键词： hybrid soft computing methods multi-objective genetic algorithms feed-forward artificial neural networks electrical drives

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Obtaining Accurate TSK Fuzzy Rule-Based Systems by multi-objective Evolutionary Learning in High-Dimensional Regression Problems

Obtaining Accurate TSK Fuzzy Rule-Based Systems by Multi-Obj...

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IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Jose Gacto, Maria Galende, Marta Alcala, Rafael Herrera, Francisco Univ Jaen Dept Comp Sci Jaen 23071 Spain CARTIF Cent Tecnolog Boecillo 47151 Spain Univ Granada Dept Comp Sci & Artificial Intelligenc E-18071 Granada Spain

ISBN: (纸本)9781479900206

This paper addresses the challenging problem of fuzzy modeling in high-dimensional and large scale regression datasets. To this end, we propose a scalable two-stage method for obtaining accurate fuzzy models in high-dimensional regression problems using approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, we propose an effective multi-objective Evolutionary Algorithm, based on an embedded genetic Data Base learning (involved variables, granularities and a slight lateral displacement of fuzzy partitions) together with an inductive rule base learning within the same process. The second stage is a post-processing process based on a second MOEA to perform a rule selection and a fine scatter-based tuning of the Membership Functions. Moreover, it incorporates an efficient Kalman filter to estimate the coefficients of the consequent polynomial functions in the Takagi-Sugeno-Kang rules. In both stages, we include mechanisms in order to significantly improve the accuracy of the model and to ensure a fast convergence in high-dimensional regression problems. The proposed method is compared to the classical ANFIS method and to a well-known evolutionary learning algorithm for obtaining accurate TSK systems in 8 datasets with different sizes and dimensions, obtaining better results.

关键词： Accurate Fuzzy Modeling multi-objective genetic algorithms Regression High-Dimensional and Large-Scale Problems

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Semi-active fuzzy control of a wind-excited tall building using multi-objective genetic algorithm

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ENGINEERING STRUCTURES 2012年第Aug.期41卷 242-257页

作者： Kim, Hyun-Su Kang, Joo-Won Yeungnam Univ Sch Architecture Gyongsan South Korea Sunmoon Univ Div Architecture Asan South Korea

In this study, a multi-objective optimal fuzzy control system for the response reduction of a wind-excited tall building has been proposed. A semi-active tuned mass damper (STMD) is used for vibration control of a 76-story benchmark building subjected to wind load. An STMD consists of a 100 kN magnetorheological (MR) damper and its natural period is tuned to the first-mode natural period of vibration of the example building structure. The damping force of the MR damper is controlled by a fuzzy logic controller. A multi-objective genetic algorithm is used for Optimization of the fuzzy logic controller. Both the 75th floor acceleration response of the structure and the stroke of the STMD have been used as the objective functions for this multi-objective optimization problem. Because a multi-objective optimization approach provides a set of Pareto-optimal solutions, an engineer is able to select an appropriate design for the specific performance requirement. For a comparative study, a sky-ground hook control algorithm is employed for control of the STMD. Based on numerical results, it has been shown that the proposed control system can effectively reduce the STMD motion as well as building responses compared to the comparative sky-ground hook control algorithm. In addition, the control performance of the STMD controlled by the optimal fuzzy controller is superior to that of the passive TMD and is comparable to an active TMD, but with a significant reduction in power consumption. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithms Wind-induced response control Semi-active tuned mass damper MR damper Fuzzy logic control system Tall building

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genetic algorithms based approach for designing spring brake orthosis - Part II: Control of FES induced movement

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APPLIED BIONICS AND BIOMECHANICS 2012年第3期9卷 317-331页

作者： Huq, M. S. Tokhi, M. O. Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K1S 5B6 Canada Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

Spring brake orthotic swing phase for paraplegic gait is initiated through releasing the brake on the knee mounted with a torsion spring. The stored potential energy in the spring, gained from the previous swing phase, is solely responsible for swing phase knee flexion. Hence the later part of the SBO operation, functional electrical stimulation (FES) assisted extension movement of the knee has to serve an additional purpose of restoring the spring potential energy on the fly. While control of FES induced movement as such is often a challenging task, a torsion spring, being antagonistically paired up with the muscle actuator, as in spring brake orthosis (SBO), only adds to the challenge. Two new schemes are proposed for the control of FES induced knee extension movement in SBO assisted swing phase. Even though the control schemes are closed-loop in nature, special attention is paid to accommodate the natural dynamics of the mechanical combination being controlled (the leg segment) as a major role playing feature. The schemes are thus found to be immune from some drawbacks associated with both closed-loop tracking as well as open-loop control of FES induced movement. A leg model including the FES knee joint model of the knee extensor muscle vasti along with the passive properties is used in the simulation. The optimized parameters for the SBO spring are obtained from the earlier part of this work. genetic algorithm (GA) and multi-objective GA (MOGA) are used to optimize the parameters associated with the control schemes with minimum fatigue as one of the control objectives. The control schemes are evaluated in terms of three criteria based on their ability to cope with muscle fatigue.

关键词： Functional electrical stimulation fuzzy logic control genetic algorithm hybrid orthosis system multi-objective genetic algorithms optimization spring brake orthosis

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Obtaining Accurate TSK Fuzzy Rule-Based Systems by multi-objective Evolutionary Learning in High-Dimensional Regression Problems

Obtaining Accurate TSK Fuzzy Rule-Based Systems by Multi-Obj...

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IEEE International Conference on Fuzzy Systems

作者： Maria Jose Gacto Marta Galende Rafael Alcala Francisco Herrera Dept. Computer Science University of Jaen CARTIF Centro Tecnologico Dept. Computer Science and Artificial Intelligence University of Granada

ISBN: (纸本)9781479900213

关键词： Accurate Fuzzy Modeling multi-objective genetic algorithms Regression High-Dimensional and Large-Scale Problems

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NSGA-II with local search for a multi-objective reactive power compensation problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 313-324页

作者： Pires, Dulce Fernao Antunes, Carlos Henggeler Martins, Antonio Comes ESTSetubal Polytech Inst Setubal P-2910761 Setubal Portugal Univ Coimbra Dept Elect Engn & Comp P-3030290 Coimbra Portugal INESC Coimbra P-3030033 Coimbra Portugal

This paper presents the results of an Elitist Non-dominated Sorting genetic Algorithm (NSGA II) enhanced with local search for computing solutions to a multi-objective reactive power compensation model. NSGA II has revealed a good performance in comparison with other multi-objective evolutionary algorithms also tested to tackle this problem. This performance is still improved using a local search scheme within NSGA II, which is specially tailored to the problem characteristics. The use of local search within the NSGA II operational framework in this complex combinatorial problem improves convergence towards the non-dominated front and ensures that the solutions attained are well spread over it. A comparative study is presented between the results obtained using a standard NSGA II based approach and the enhanced NSGA II approach with local search, to provide decision support in the VAR planning problem in radial electric distribution systems. The model explicitly considers two objective functions concerning economical and operational evaluation aspects. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective models Electric distribution networks Reactive power compensation multi-objective genetic algorithms Elitist Non-dominated Sorting genetic Algorithm (NSGA II)

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