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A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

ENGINEERING OPTIMIZATION 2007年第8期39卷 877-898页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Dept Ind Engn Tehran Iran

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective particle swarm tabu search multi-objective genetic algorithm

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A multi-objective scatter search for a mixed-model assembly line sequencing problem

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ADVANCED ENGINEERING INFORMATICS 2007年第1期21卷 85-99页

作者： Rahimi-Vahed, A. R. Rabbani, M. Tavakkoli-Moghaddam, R. Torabi, S. A. Jolai, F. Univ Tehran Dept Ind Engn Tehran 4563 Iran

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： mixed-model assembly line multi-objective sequencing problem just-in-time multi-objective scatter search multi-objective genetic algorithm

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Structural multi-objective Probabilistic Design for Six Sigma

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Journal of Shanghai Jiaotong university(Science) 2007年第1期12卷 100-105页

作者：李玉强崔振山陈军张冬娟阮雪榆 National Die and Mold CAD Eng.Research Center Shanghai Jiaotong Univ. Dept. of Die Design Shanghai Sekely Die Technology Co. Ltd. Shanghai 201209 China

Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in various design environments. In order to obtain solutions that are not only "multi-objectively" optimal, but also reliable and robust, a probabilistic optimization method was presented by integrating six sigma philosophy and multi-objective genetic algorithm. With this method, multi-objective genetic algorithm was adopted to obtain the global Pareto solutions, and six sigma method was used to improve the reliability and robustness of those optimal solutions. Two engineering design problems were provided as examples to illustrate the proposed method.

关键词： multi-objective genetic algorithm six sigma reliability-based design optimization robust design

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Iris recognition based on zigzag collarette region and asymmetrical support vector machines

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4th International Conference on Image Analysis and Recognition

作者： Roy, Kaushik Bhattacharya, Prabir Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783540742586

This paper presents an iris recognition technique based on the zigzag collarette region for segmentation and asymmetrical support vector machine to classify the iris pattern. The deterministic feature sequence extracted from the iris images using the 1D log-Gabor filters is applied to train the support vector machine (SVM). We use the multi-objective genetic algorithm (MOGA) to optimize the features and also to increase the overall recognition accuracy based on the matching performance of the tuned SVM. The traditional SVM is modified to an asymmetrical SVM to treat the cases of the False Accept and the False Reject differently and also to handle the unbalanced data of a specific class with respect to the other classes. The proposed technique is computationally effective with recognition rates of 97.70 % and 95.60% on the ICE (Iris Challenge Evaluation) and the WVU (West Virginia University) iris datasets respectively.

关键词： biometrics asymmetrical support vector machine multi-objective genetic algorithm zigzag collarette region

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CFD-based multi-objective optimization method for ship design

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS 2006年第5期52卷 499-527页

作者： Tahara, Yusuke Tohyama, Satoshi Katsui, Tokihiro Osaka Prefecture Univ Sakai Osaka 591 Japan

This paper concerns development and demonstration of a computational fluid dynamics (CFD)-based multi-objective optimization method for ship design. Three main components of the method, i.e. computer-aided design (CAD), CFD, and optimizer modules are functionally independent and replaceable. The CAD used in the present study is NAPA system, which is one of the leading CAD systems in ship design. The CFD method is FLOWPACK version 2004d, a Reynolds-averaged Navier-Stokes (RaNS) solver developed by the present authors. The CFD method is implemented into a self-propulsion simulator, where the RaNS solver is coupled with a propeller-performance program. In addition, a maneuvering simulation model is developed and applied to predict ship maneuverability performance. Two nonlinear optimization algorithms are used in the present study, i.e. the successive quadratic programming and the multi-objective genetic algorithm, while the former is mainly used to verify the results from the latter. For demonstration of the present method, a multi-objective optimization problem is formulated where ship propulsion and maneuverability performances are considered. That is, the aim is to simultaneously minimize opposite hydrodynamic performances in design tradeoff. In the following, an overview of the present method is given, and results are presented and discussed for tanker stern optimization problem including detailed verification work on the present numerical schemes. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： CAD RaNS solver hull-form optimization multi-objective genetic algorithm maneuverability propulsion

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Prediction of building's temperature using neural networks models

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ENERGY AND BUILDINGS 2006年第6期38卷 682-694页

作者： Ruano, AE Crispim, EM Conceiçao, EZE Lúcio, MMJR Univ Algarve Fac Sci & Technol Ctr Intelligent Syst P-8005139 Faro Portugal Univ Algarve Fac Marine & Environm Sci P-8005139 Faro Portugal Direcc Reg Educ Algarve Sitio Figuras P-8000761 Faro Portugal

The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. In this paper the design of inside air temperature predictive neural network models, to be used for predictive control of air-conditioned systems, is discussed. The use of multi-objective genetic algorithms for designing off-line radial basis function neural network models is detailed. The performance of these data-driven models is compared, favourably, with a multi-node physically based model. Climate and environmental data from a secondary school located in the south of Portugal, collected by a remote data acquisition system, are used to generate the models. By using a sliding window adaptive methodology, the good results obtained off-line are extended throughout the whole year. The use of long-range predictive models for air-conditioning systems control is demonstrated, in simulations, achieving a good temperature regulation with important energy savings. (C) 2005 Elsevier B.V. All rights reserved.

关键词： temperature prediction neural networks multi-objective genetic algorithm radial basis function networks

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Applying multi-objective genetic algorithms in green building design optimization

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BUILDING AND ENVIRONMENT 2005年第11期40卷 1512-1525页

作者： Wang, WM Zmeureanu, R Rivard, H Concordia Univ Ctr Bldg Studies Dept Bldg & Environm Engn Montreal PQ H3G 1M8 Canada Ecole Technol Super Dept Construct Engn Montreal PQ Canada

Since buildings have considerable impacts on the environment, it has become necessary to pay more attention to environmental performance in building design. However, it is a difficult task to find better design alternatives satisfying several conflicting criteria, especially, economical and environmental performance. This paper presents a multi-objective optimization model that could assist designers in green building design. Variables in the model include those parameters that are usually determined at the conceptual design stage and that have critical influence on building performance. Life cycle analysis methodology is employed to evaluate design alternatives for both economical and environmental criteria. Life cycle environmental impacts are evaluated in terms of expanded cumulative exergy consumption, which is the sum of exergy consumption due to resource inputs and abatement exergy required to recover the negative impacts due to waste emissions. A multi-objective genetic algorithm is employed to find optimal solutions. A case study is presented and the effectiveness of the approach is demonstrated for identifying a number of Pareto optimal solutions for green building design. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： building design green building life cycle assessment life cycle cost multi-objective genetic algorithm

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BI-CRITERIA SCHEDULING ON A BATCHING MACHINE WITH NON-IDENTICAL JOB SIZES

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IFAC Proceedings Volumes 2006年第3期39卷 727-732页

作者： Ali Husseinzadeh Kashan Behrooz Karimi Fariborz Jolai Department of Industrial Engineering Amirkabir University of Technology P.O.Box: 15875-4413 Tehran Iran Department of Industrial Engineering Faculty of Engineering University of Tehran P.O.Box: 11365-4563 Tehran Iran

This paper addresses scheduling jobs with non-identical sizes on a single batch-processing machine. Two different multi-objective genetic algorithms (MOGA) have been proposed. The first one is a sequence based MOGA (SMOGA) that generates random sequences of jobs and applies the batch first fit heuristic to group the jobs. The second one is a batch based hybrid MOGA (BHMOGA) that generates random batches of jobs and ensures feasibility through using knowledge of the problem. Computational results show that non-dominated solutions obtained by BHMOGA are superior in closeness to the true Pareto-optimal solutions and to keep diversity in the obtained Pareto-set, as the problem size increases.

关键词： Scheduling Batch processing machine multi-objective genetic algorithm Makespan Maximum tardiness

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multi-objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions

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TIRE SCIENCE AND TECHNOLOGY 2006年第3期34卷 170-194页

作者： Koishi, M. Shida, Z. Yokohama Rubber Co Ltd 2-1 Oiwake Hiratsuka Kanagawa 2548601 Japan

Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called "multi-performan

关键词： inverse problem multi-objective optimization multi-objective genetic algorithm self-organizing map Pareto solution wear finite element analysis

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A new method to construct the non-dominated set in multi-objective genetic algorithms

A new method to construct the non-dominated set in multi-obj...

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2nd International Conference on Intelligent Information Processing

作者： Zheng, JH Shi, ZZ Ling, CX Xie, Y Xiangtan Univ Coll Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)038723151X

There have been widespread applications for multi objective genetic algorithm (MOGA) on highly complicated optimization tasks in discontinuous, multi-modal, and noisy domains. Because the convergence of MOGA can be reached with the non-dominated set approximating the Pareto Optimal front, it is very important to construct the non-dominated set of MOGA efficiently. This paper proposes a new method called Dealer's Principle to construct non-dominated sets of MOGA, and the time complexity is analyzed. Then we design a new MOGA with the Dealer's Principle and a clustering algorithm based on the core distance of clusters to keep the diversity of solutions. We show that our algorithm is more efficient than the previous algorithms, and that it produces a wide variety of solutions. We also discuss the convergence and the diversity of our MOGA in experiments with benchmark optimization problems of three objectives.

关键词： multi-objective genetic algorithm multi-objective optimum dominated relationship non-dominated set

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