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A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness

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INFORMATION SCIENCES 2007年第22期177卷 5072-5090页

作者： Tavakkoli-Moghaddam, Reza Rahimi-Vahed, Alireza Mirzaei, Ali Hossein Univ Tehran Fac Engn Dept Ind Engn Tehran Iran Tarbiat Modares Univ Dept Ind Engn Tehran Iran

This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time ((C) over bar (w)) and weighted mean tardiness ((T) over bar (w)). Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems. (C) 2007 Elsevier Inc. All rights reserved.

关键词： multi-objective no-wait flow shop scheduling completion time tardiness multi-objective evolutionary algorithms immune system bacterial optimization

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An uncertain structural optimization method based on nonlinear interval number programming and interval analysis method

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ENGINEERING STRUCTURES 2007年第11期29卷 3168-3177页

作者： Jiang, C. Han, X. Guan, F. J. Li, Y. H. Hunan Univ Coll Mech & Automot Engn State Key Lab Adv Design & Manufacture Vehicle Bo Changsha 410082 Peoples R China

An optimization method is proposed to solve uncertain structural problems based on a nonlinear interval number programming method and an interval analysis method. A nonlinear interval number programming method is suggested to transform the uncertain optimization problem to a deterministic multi-objective optimization problem based on an order relation of interval. For each specific design vector, an interval analysis method is applied to calculate the interval of the objective function caused by uncertainty, and whereby the optimization nesting problem can be solved. A non-constraint and single-objective optimization problem is then formulated through the linear combination method of multi-objective optimization and the penalty function method. An intergeneration projection genetic algorithm is employed to seek for Pareto optimum of the uncertain problem. The presented method is applied to a benchmark test of ten-bar truss and a practical automobile frame, and the optimization results demonstrate its efficiency. (c) 2007 Published by Elsevier Ltd.

关键词： uncertainty nonlinear interval number programming interval analysis method genetic algorithm structural optimization

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基于改进遗传算法的无源滤波器优化设计

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湖南科技学院学报 2008年第4期29卷 93-96页

作者：周君求陈凯邵阳学院电气与信息工程系湖南省邵阳市422000

针对目前根据工程经验或简单的技术经济指标来设计无源滤波器的现状,以及现有优化设计方法中假设条件较多、寻优空间较小、寻优能力不强、对实际因素考虑不全面等问题,提出了一种改进的遗传设计方法。将无源滤波器的初期投资、无功功率... 详细信息

针对目前根据工程经验或简单的技术经济指标来设计无源滤波器的现状,以及现有优化设计方法中假设条件较多、寻优空间较小、寻优能力不强、对实际因素考虑不全面等问题,提出了一种改进的遗传设计方法。将无源滤波器的初期投资、无功功率补偿容量、滤波后电网谐波含量作为三个目标,利用遗传算法对无源滤波器的参数进行优化设计。通过适应度函数的阈值制约以及以不同概率进行染色体选择操作,使得种群朝三个目标最佳协调点的方向演化;为克服算法的早熟收敛问题也进行了有益探讨。最后对一个混合滤波实验系统进行了无源滤波参数的优化设计,并和传统的方法进行了比较。仿真和实验表明设计出的无源滤波器具有良好的综合性能。

关键词：函数无源滤波器:多目标优化遗传算法

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Concurrent multi-objective optimization of plug-in parallel HEV by a hybrid evolution algorithm

Concurrent multi-objective optimization of plug-in parallel ...

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作者： Wang, Qing University of California Davis

学位级别：Ph.D.

This dissertation presents a new method for solving for the optima of the Plug-in HEV's overall system parameters. Different from the existing HEV optimization approaches shown in the literature that are mainly control strategies focused, our study suggested that the powertrain sizing optimization is also a crucial factor for achieving minimum fuel consumption and emissions. To solve this multi-objective problem, the dissertation research featured a concurrent approach that simultaneously optimizes both HEV powertrain sizing parameters and control logics. The novelty is using probabilistic algorithms to attack this large-scale and nonlinear problem. Such a derivative-free approach has gained high efficiency in handling the high-order, noisy and discontinuous objective functions, and nonlinear constraints of the Plug-in HEV optimization problem. A generic design methodology of parameterizing the optimal propulsion system for the Plug-in Parallel HEV has been developed in the course of this research. It was divided into the four stages: designing search algorithms, building the cost models, analyzing multiple constraints, and implementing the optimization. Based on the modeling of Plug-in HEV and CVT (Continuously Variable Transmission) control, we implemented two global probabilistic algorithms, Genetic Algorithm and Simulated Annealing, into a case study of a parallel hybrid medium duty Step Van. Promising results or concurrent optimization have been achieved through simulations. They reveal that both algorithms are practical and effective but have certain limitations. To further enhance the overall search performance, we designed a Hybrid Evolution Algorithm that combines the strengths and overcomes the shortcomings of Genetic Algorithm and Simulated Annealing. Such an algorithm hybridization of both techniques inherits merits of each other and further enhances the overall search speed and accuracy. The overall optimization scheme simultaneously optimizes the param

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Reference Point approaches and objective Ranking

Reference Point Approaches and Objective Ranking

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practical approaches to multi-objective optimization 2006

作者： Wierzbicki, Andrzej P. Center for Strategic Development of Science and Technology JAIST 1-1 Asahidai Ishikawa Nomi923-1292 Japan National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

The paper presents a reflection on some of the basic assumptions and philosophy of reference point approaches, stressing their unique concentration on the sovereignty of the subjective decision maker. As a new development in reference point approaches also the concept of objective ranking is stressed, defined as dependent only on a given set of data, relevant for the decision situation, and independent from any more detailed specification of personal preferences than that given by defining criteria and the partial order in criterion space. Rational objective ranking can be based on reference point approach, because reference levels needed in this approach can be established objectively statistically from the given data set. Examples show that such objective ranking can be very useful in many management situations. © 2007 Dagstuhl Seminar Proceedings. All rights reserved.

关键词： Decision making

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Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2007年第4期1卷 586-597页

作者： Figueiredo, Mario A. T. Nowak, Robert D. Wright, Stephen J. Inst Telecommun P-1049001 Lisbon Portugal Inst Super Tecn Dept Elect & Comp Engn P-1049001 Lisbon Portugal Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared l(2)) error term combined with a sparseness-inducing (l(1)) regularization term. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution, and compressed sensing are a few well-known examples of this approach. This paper proposes gradient projection (GP) algorithms for the bound-constrained quadratic programming (BCQP) formulation of these problems. We test variants of this approach that select the line search parameters in different ways, including techniques based on the Barzilai-Borwein method. Computational experiments show that these GP approaches perform well in a wide range of applications, often being significantly faster (in terms of computation time) than competing methods. Although the performance of GP methods tends to degrade as the regularization term is de-emphasized, we show how they can be embedded in a continuation scheme to recover their efficient practical performance.

关键词： Compressed sensing convex optimization deconvolution gradient projection quadratic programming sparseness sparse reconstruction

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A general concept for solving linear multicriteria programming problems with crisp, fuzzy or stochastic values

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FUZZY SETS AND SYSTEMS 2007年第17期158卷 1892-1904页

作者： Rommelfanger, Heinrich JW Goethe Univ Frankfurt Inst Stat & Math D-60054 Frankfurt Germany

For modelling imprecise data the literature offers two different methods: either the use of probability distributions or the use of fuzzy sets. In our opinion these two concepts should be used in parallel or in combination, dependent on the real situation. Moreover, in many economic problems, the well-known probabilistic or fuzzy solution procedures are not suitable because the stochastic variables do not have a simple classical distribution and the fuzzy values are not fuzzy intervals. For example, in case of investment problems the coefficients may often be described by means of more complex distributions or more general fuzzy sets. In this case we propose to distinguish several scenarios and to describe the parameters of the different scenarios by fuzzy intervals. For solving such stochastic linear programs with fuzzy parameters we propose a new method, which retains the original objective functions dependent on the different states of nature. It is based on the integrated chance constrained program introduced by Klein Haneveld [On integrated chance constraints, in: Gargnano (Ed.), Stochastic Programming, Springer, Berlin, 1986, pp. 194-209] and the interactive solution process FULPAL, see Rommelfanger [Fuzzy Decision Support-Systeme - Entscheiden bei Unschdife, second ed., Springer, Berlin, Heidelberg, 1994;FULPAL: an interactive method for solving multiobjective fuzzy linear programming problems, in: R. Slowinski, J. Teghem (Eds.), Stochastic Versus Fuzzy approaches to multiobjective Mathematical Programming under Uncertainty, Reidel Publishing Company, Dordrecht, 1990, pp. 279-299;FULPAL 2.0-an interactive algorithm for solving multicriteria fuzzy linear programs controlled by aspiration levels, in: D. Scheigert (Ed.), Methods of multicriteria Decision Theory, Pfalzakademie Lamprecht, 1995, pp. 21-34;The advantages of fuzzy optimization models in practical use, Fuzzy Optim. Decision Making 3 (2004) 295-3 10] and Rommelfanger and Slowinski [Fuzzy linear program

关键词： fuzzy multicriteria optimization Stochastic programming interactive decision process investment problems

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Data Clustering of Solutions for multiple objective System Reliability optimization Problems

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Quality Technology & Quantitative Management 2007年第2期4卷 191-210页

This paper proposes a practical methodology for the solution of multi-objective system reliability optimization problems. The new method is based on the sequential combination of multi-objective evolutionary algorithms and data clustering on the prospective solutions to yield a smaller, more manageable sets of prospective solutions. Existing methods for multiple objective problems involve either the consolidation of all objectives into a single objective, or the determination of a Pareto-optimal set. In this paper, a new approach, involving post-Pareto clustering is proposed, offering a compromise between the two traditional approaches. In many real-life multi-objective optimization problems, the Pareto-optimal set can be extremely large or even contain an infinite number of solutions. Broad and detailed knowledge of the system is required during the decision making process in discriminating among the solutions contained in the Pareto-optimal set to eliminate the less satisfactory trade-offs and to select the most promising solution(s) for system implementation. The well-known reliability optimization problem, the redundancy allocation problem (RAP), was formulated as a multi-objective problem with the system reliability to be maximized, and cost and weight of the system to be minimized. A multiple stage process was performed to identify promising solutions. A Pareto-optimal set was initially obtained using the fast elitist nondominated sorting genetic algorithm (NSGA-II). The decision-making stage was then performed with the aid of data clustering techniques to prune the size of the Pareto-optimal set and obtain a smaller representation of the multi-objective design space; thereby making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

关键词： Data mining multiple objective genetic algorithms (MOGA) system reliability optimization

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Simulation-based optimization of an active noise and vibration control solution

Simulation-based optimization of an active noise and vibrati...

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25th Conference and Exposition on Structural Dynamics 2007, IMAC-XXV

作者： Van Der Auweraer, Herman Mas, Peter De Oliveira, Leopoldo Da Silva, Maira Desmet, Wim LMS International Leuven Belgium K.U.Leuven PMA Division Leuven Belgium

ISBN: (纸本)9781604237597

Active control appears a feasible solution to many noise and vibration problems. To bring present research results to industrial use, the related design approach must become part of the industrial product creation process. This requires the product's CAE models to support the specific aspects related to advanced materials, actuators, sensors and control. To be of practical use in solving industrial problems, a constraint is that the simulations must as much as possible make use of standard available simulation tools such as major FE/BE and multiBody Simulation (MBS) codes, 1-D simulation tools etc. The most challenging element hereto is to link the different worlds of 1-D control simulation and 3-D geometry-based structural/vibro-acoustic simulation into system-level models. The paper addresses as main approach the reduction of the structural & vibro-acoustic models into a 1-D state space representation. Alternative approaches such as the integration of control in FE and the co-simulation between structural and control models are also briefly addressed. The applied approach is demonstrated on the InMAR "Concrete Car", where the optimization of the control approach for an active firewall solution is performed, taking into account multi-objective design criteria. The research is executed in the context of the EC-FP6 Integrated Project InMAR.

关键词： optimization

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Extending Graph-Cut to Continuous Value Domain Minimization

Extending Graph-Cut to Continuous Value Domain Minimization

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Canadian Conference on Computer and Robot Vision, CRV

作者： Michael Felsberg Computer Vision Laboratory Linköping University Sweden

In this paper we propose two methods for minimizing objective functions of discrete functions with continuous value domain. Many practical problems in the area of computer vision are continuous-valued, and discrete optimization methods of graph-cut type cannot be applied directly. This is different with the proposed methods. The first method is an add-on for multiple-label graph-cut. In the second one, binary graph-cut is firstly used to generate regions of support within different ranges of the signal. Secondly, a robust error minimization is approximated based on the previously determined regions. The advantages and properties of the new approaches are explained and visualized using synthetic test data. The methods are compared to ordinary multi-label graph-cut and robust smoothing for the application of disparity estimation. They show better quality of results compared to the other approaches and the second algorithm is significantly faster than multi-label graph-cut.

关键词： Computer vision Smoothing methods Minimization methods Laboratories optimization methods Signal generators Data visualization Testing Noise robustness Clustering algorithms

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