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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Rathod, Vijay Yadav, Om Prakash Rathore, Ajay Jain, Rakesh Malaviya Natl Inst Technol Dept Mech Engn Jaipur Rajasthan India Norh Dakota State Univ Dept Ind & Mfg Engn Fargo ND USA

ISBN: (纸本)9781457707391

Reliability-based robust design optimization (RBRDO) is one of the most essential tools developed in recent years to improve the quality and reliability of the products at an early design stage. This paper presents a comparative study of different formulation approaches of RBRDO models and their performances. The paper also proposes an evolutionary multi-objective genetic algorithm (MOGA) to one of the promising hybrid quality loss functions (HQLF)-based RBRDO model. The enhanced effectiveness of the RBRDO model is demonstrated by optimizing suitable example.

关键词： Reliability-based design optimization robust design optimization multi-objective genetic algorithms

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Current Control Design with a Fractional-Order PID for a Three-Level Inverter

Current Control Design with a Fractional-Order PID for a Thr...

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14th European Conference on Power Electronics and Applications (EPE)/ECCE Europe Conference on Power Electronics and Adjustable Speed Drives - Towards the 20-20-20 Target

作者： Tehrani, Kambiz-Arab Capitaine, Thierry Barrandon, Ludovic Hamzaoui, Mohammed Rafiei, S. M. R. Lebrun, Andre Univ Picardie UPJV INSSET Inst Sci & Technol F-80025 Amiens France Univ Turin Turin Italy

ISBN: (纸本)9789075815153

In this paper, a fractional-order proportional-integral-derivative (FOPID) controller is investigated for a three-level inverter called multi-Neutral Point (MNP). For the first time, an FOPID is applied to multilevel inverters. Our main objectives are to remove the amplitude error, the phase shift error and decreasing the total harmonic distortion of the current.

关键词： multilevel Inverter MNP PID FOPID multi-objective genetic algorithms

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Selecting Model Parameter Sets from a Trade-off Surface Generated from the Non-Dominated Sorting genetic Algorithm-II

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WATER RESOURCES MANAGEMENT 2010年第15期24卷 4469-4489页

作者： Dumedah, Gift Berg, Aaron A. Wineberg, Mark Collier, Robert Univ Guelph Dept Geog Guelph ON N1G 2W1 Canada Univ Guelph Dept Comp & Informat Sci Guelph ON N1G 2W1 Canada

There is increasing trend in the use of multi-objective genetic algorithms (GAs) to estimate parameter sets in the calibration of hydrological models multi-objective GAs facilitate the evaluation of several model evaluation objectives, and the examination of massive combinations of parameter sets Typically, the outcome is a set of several equally-accurate parameter sets which make-up a trade-off surface between the objective functions, usually referred to as Pareto set The Pareto set is a set of incomparable parameter sets as each solution has unique parameter values in parameter space with competing accuracy in the objective function space As would be required for decision making purposes, a single parameter set is usually chosen to represent the model calibration procedure An automated framework for choosing a single solution from such a trade-off surface has not been thoroughly investigated in the model calibration literature As a result, this study has outlined an automated framework using the distribution of solutions in objective space and parameter space to select solutions with unique properties from an incomparable set of solutions Our Pareto set was generated from the application of Non-dominated Sorting genetic Algorithm-II (NSGA-II) to calibrate the Soil and Water Assessment Tool (SWAT) for simulations of streamflow in the Fairchild Creek watershed in southern Ontario Using cluster analysis to evaluate the distribution of solutions in both objective space and parameter space, we developed four auto-selection methods for choosing parameter sets from the trade-oft surface to support decision making Our method generates solutions with unique properties including a representative pathway m parameter space, a basin of attraction (or the center of mass) in objective space, a proximity to the origin in objective space, and a balanced compromise between objective space and parameter space (denoted BCOP) The BCOP method is appealing as it is an equally-weighted c

关键词： Hydrological calibration model multi-objective genetic algorithms NSGA-II Pareto set Robust

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Application of multi-objective genetic algorithms to multireservoir system optimization in the Han River basin

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KSCE Journal of Civil Engineering 2006年第5期10卷 371-380页

作者： Taesoon Kim Jun-Haeng Heo School of Civil and Environmental Engineering Yonsei University 120-749 Seoul Korea

The objective of multireservoir system optimization is to achieve an optimal reservoir operating plan by the effective use of water resources. Many optimization techniques have been applied for the last decades, and researchers have recently interested in the heuristic approaches like evolutionary computation. This study proposes a methodology for applying multi-objective genetic algorithms (MOGAs) to a multireservoir system optimization in the Han River basin. The second generation evolutionary multiobjective technique, NSGA-II, is used. The simulation model is applied to the Han River basin and the performance of the model is compared with the historical reservoir operation records. Two different cases are performed to evaluate the applicability of NSGA-II. Case 1 shows the basic performance of NSGA-II as applied to multireservoir system optimization, and Case 2 presents the methodology to discriminate the critical decision variables. In addition, the alternative releases and storages by NSGA-II are compared with the historical releases and storages. Cases 1 and 2 show that NSGA-II can be applied to multireservoir system optimization, and the alternative releases and storages computed using the results from NSGA-II can be used as the possible resrvoir operating plans that supply more water resources to downstream than the historical releases.

关键词： multireservoir system optimization genetic algorithms multi-objective genetic algorithms evolutionary computation NSGA-II

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MOGAMOD: multi-objective genetic algorithm for motif discovery

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 1039-1047页

作者： Kaya, Mehmet Firat Univ Dept Comp Engn TR-23119 Elazig Turkey

We propose all efficient method using multi-objective genetic algorithm (MOGAMOD) to discover optimal motifs in sequential data. The main advantage of our approach is that a large number of tradeoff (i.e., nondominated) motifs call be obtained by a single run with respect to conflicting objectives: similarity, motif length and support maximization. To the best of our knowledge, this is the first effort in this direction. MOGAMOD call be applied to any data set with a sequential character, Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the obtained optimal motifs, the decision maker call understand the tradeoff between the objectives. We compare MOGAMOD with the three well-known motif discovery methods, AlignACE, MEME and Weeder. Experimental results on real data set extracted from TRANSFAC database demonstrate that the proposed method exhibits good performance over the other methods in terms of accuracy and runtime. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： Motif discovery multi-objective genetic algorithms Transcription factors

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Coordination of set-ups between two stages of a supply chain using multi-objective genetic algorithms

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2005年第15期43卷 3163-3180页

作者： Mansouri, SA Amirkabir Univ Technol Dept Ind Engn Tehran 15914 Iran

A multi-objective genetic algorithm (MOGA) solution approach for a sequencing problem to coordinate set-ups between two successive stages of a supply chain is presented in this paper. The production batches are processed according to the same sequence in both stages. Each production batch has two distinct attributes and a set-up occurs in the upstream stage every time the first attribute of the new batch is different from the previous one. In the downstream stage, there is a set-up when the second attribute of the new batch is different from that of the previous one. Two objectives need to be considered in sequencing the production batches including minimizing total set-ups and minimizing the maximum number of set-ups between the two stages. Both problems are NP-hard so attainment of an optimal solution for large problems is prohibited. The solution approach starts with an initialization stage followed by evolution of the initial solution set over generations. The MOGA makes use of non-dominated sorting and a niche mechanism to rank individuals in the population. Selected individuals taken from a given population form the succeeding generation using four genetic operators as: reproduction, crossover, mutation and inversion. Experiments in a number of test problems show that the MOGA is capable of finding Pareto-optimal solutions for small problems and near Pareto-optimal solutions for large instances in a short CPU time.

关键词： set-up coordination sequencing supply chains multi-objective genetic algorithms

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Embedded genetic Learning of Highly Interpretable Fuzzy Partitions

Embedded Genetic Learning of Highly Interpretable Fuzzy Part...

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Joint International-Fuzzy-Systems-Association World Congress/European-Society-Fuzzy-Logic-and-Technology Conference

作者： Casillas, Jorge Univ Granada CITIC UGR Res Ctr Commun & Informat Technol Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

ISBN: (纸本)9789899507968

A new algorithm is proposed to learn fuzzy partitions with a high interpretability degree. The set of input variables, the number of linguistic terms per variable, and the type (triangular or trapezoidal) and parameters of the membership functions are learnt by means of a meta-algorithm that uses a simple learning method to generate a fuzzy rule set from the derived fuzzy partitions. Interpretability constrains and powerful genetic operators are considered. A multi-objective optimization approach is used to generate different interpretability-accuracy tradeoffs. The algorithm is tested in a set of real-world regression problems with successful results compared to other methods.

关键词： Fuzzy rule-based systems fuzzy partitions granularity interpretability multi-objective genetic algorithms

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genetic Learning of Serial Hierarchical Fuzzy Systems for Large-Scale Problems

Genetic Learning of Serial Hierarchical Fuzzy Systems for La...

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Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT)

作者： Benitez, Alicia D. Casillas, Jorge Univ Granada Dept Comp Sci & Artificial Intelligence CITIC UGR Res Ctr Informat & Commun Technol E-18071 Granada Spain

ISBN: (纸本)9789899507968

When we face a problem with a high number of variables by a standard fuzzy system, the number of rules increases exponentially and then the obtained fuzzy system is scarcely interpretable. This problem can be handled by arranging the inputs in hierarchical ways. The paper presents a multi-objective genetic Algorithm that learns Serial Hierarchical Fuzzy Systems with the aim of coping the curse of dimensionality. By means of an experimental study, we have observed that our algorithm obtains good results of interpretability and precision with problems in which the number of variables is relatively high.

关键词： Curse of dimensionality hierarchical fuzzy systems multi-objective genetic algorithms variable selection

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Applied Pareto multi-objective optimization by stochastic solvers

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2009年第3期22卷 455-465页

作者： Martinez-Iranzo, Miguel Herrero, Juan M. Sanchis, Javier Blasco, Xavier Garcia-Nieto, Sergio Univ Politecn Valencia Dept Syst Engn & Control Predict Control & Heurist Optimizat Grp Valencia 46022 Spain

It is well known that many engineering design problems with different objectives, some of which can be opposed to one another, can be formulated as multi-objective functions and resolved with the construction of a Pareto front that helps to select the desired solution. Obtaining a correct Pareto front is not a trivial question, because it depends on the complexity of the objective functions to be optimized, the constraints to keep within and, in particular, the optimizer type selected to carry out the calculations. This paper presents new methods for Pareto front construction based on stochastic search algorithms (genetic algorithms, GAs and multi-objective genetic algorithms, MOGAs) that enable a very good determination of the Pareto front and fulfill some interesting specifications. The advantages of these applied methods will be proven by the optimization of well-known benchmarks for metallic supported I-beam and gearbox design. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization Pareto front Engineering design genetic algorithms multi-objective genetic algorithms

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A multi-objective scatter search for a dynamic cell formation problem

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COMPUTERS & OPERATIONS RESEARCH 2009年第3期36卷 777-794页

作者： Bajestani, M. Aramoon Rabbani, M. Rahimi-Vahed, A. R. Khoshkhou, G. Baharian Univ Tehran Dept Ind Engn Tehran 4563 Iran

Cellular manufacturing system-an important application of group technology (GT)-has been recognized as an effective way to enhance the productivity in a factory. Consequently, a multi-objective dynamic cell formation problem is presented in this paper, where the total cell load variation and sum of the miscellaneous costs (machine cost, inter-cell material handling cost, and machine relocation cost) are to he minimized simultaneously. Since this type of problem is NP-hard. a new multi-objective scatter search (MOSS) is designed for finding locally Pareto-optimal frontier. To demonstrate the efficiency of the proposed algorithm, MOSS is compared with two salient multi-objective genetic algorithms, i.e. SPEA-II and NSGA-II based on some comparison metrics and statistical approach. The computational results indicate the superiority of the proposed MOSS compared to these two genetic algorithms. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： multi-objective cell formation problem Dynamic cell formation multi-objective scatter search multi-objective genetic algorithms

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