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  • Open Access

    ARTICLE

    Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001

    Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes… More >

  • Open Access

    ARTICLE

    A Non-probabilistic Reliability-based Optimization of Structures Using Convex Models

    Fangyi Li1,2, Zhen Luo3, Jianhua Rong1, Lin Hu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.95, No.6, pp. 453-482, 2013, DOI:10.3970/cmes.2013.095.453

    Abstract This paper aims to propose a non-probabilistic reliability-based multiobjective optimization method for structures with uncertain-but-bounded parameters. A combination of the interval and ellipsoid convex models is used to account for the different groups of uncertain parameters, in which the interval model accounts for uncorrelated parameters, while the ellipsoid model is applied to correlated parameters. The design is then formulated as a nested double-loop optimization problem. A multi-objective genetic algorithm is used in the out loop optimization to optimize the design vector for evaluating the objectives, and the Sequential Quadratic Programming (SQP) algorithm is applied in… More >

  • Open Access

    ABSTRACT

    Direct Interval Multi-Objective Optimization Method for Uncertain Structures

    Guiping Liu*, Sheng Liu

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.4, pp. 92-92, 2019, DOI:10.32604/icces.2019.05523

    Abstract In engineering multi-objective optimization of structures, the parameters involved in the problems are usually given deterministic values. However, due to the presence of manufacturing and measurement errors, uncertainty inevitably exists in the geometrical properties of the structure, the material properties, the boundary conditions, etc. For uncertain problems, the interval optimization methods are widely used. They describe the uncertainty by intervals which only need to find the upper and lower bounds of the uncertain parameters instead of constructing the exact probability distribution function. However, in multi-objective optimization problems, if considered all the upper and lower bounds… More >

  • Open Access

    ARTICLE

    A New Multi-objective Reliability-based Robust Design Optimization Method

    Zichun Yang1,2, Maolin Peng1,3,4, Yueyun Cao1, Lei Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.98, No.4, pp. 409-442, 2014, DOI:10.3970/cmes.2014.098.409

    Abstract A new multi-objective reliability-based robust design optimization (M ORBRDO) model is proposed which integrats the multi-objective robustness, the reliability sensitivity robustness and the six sigma robustness design idea. The pure-quadratic polynomial functions are adopted to fit the performance objective functions (POF) and the ultimate limited state functions (ULSF) of the structure. Based on the ULSF and the checking point method, the equations of the first order reliability index are calculated. The mapping transformation method is employed when the non-normal distribution variables are included. According to the POF and the Taylor series expansion method, the equations… More >

  • Open Access

    ARTICLE

    Multi-Objective Optimization of a Fluid Structure Interaction Benchmarking

    M. Razzaq1, C. Tsotskas2, S. Turek1, T. Kipouros2, M. Savill2, J. Hron3

    CMES-Computer Modeling in Engineering & Sciences, Vol.90, No.4, pp. 303-337, 2013, DOI:10.3970/cmes.2013.090.303

    Abstract The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed fluid-structure interaction benchmark which describes the self-induced elastic deformation of a beam More >

  • Open Access

    ARTICLE

    The Design of Multi-Element Airfoils Through Multi-Objective Optimization Techniques

    G. Trapani1, T. Kipouros1, A. M. Savill1

    CMES-Computer Modeling in Engineering & Sciences, Vol.88, No.2, pp. 107-140, 2012, DOI:10.3970/cmes.2012.088.107

    Abstract This paper presents the development and the application of a multi-objective optimization framework for the design of two-dimensional multi-element high-lift airfoils. An innovative and efficient optimization algorithm, namely Multi-Objective Tabu Search (MOTS), has been selected as core of the framework. The flow-field around the multi-element configuration is simulated using the commercial computational fluid dynamics (cfd) suite Ansys cfx. Elements shape and deployment settings have been considered as design variables in the optimization of the Garteur A310 airfoil, as presented here. A validation and verification process of the cfd simulation for the Garteur airfoil is performed… More >

  • Open Access

    ARTICLE

    Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization

    T. Kipouros1, D.M. Jaeggi2, W.N. Dawes3, G.T. Parks2,A.M. Savill1, P.J. Clarkson2

    CMES-Computer Modeling in Engineering & Sciences, Vol.37, No.1, pp. 1-44, 2008, DOI:10.3970/cmes.2008.037.001

    Abstract An approach to support the computational aerodynamic design process is presented and demonstrated through the application of a novel multi-objective variant of the Tabu Search optimization algorithm for continuous problems to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and ultimately of the whole engine. The integrated system developed for this purpose is described. This combines the optimizer with an existing geometry parameterization scheme and a well-established CFD package. The system's performance is illustrated through case studies -- one two-dimensional, one three-dimensional -- in which… More >

  • Open Access

    ARTICLE

    Multidisciplinary Design Optimization of Long Endurance Unmanned Aerial Vehicle Wing

    S. Rajagopal1, Ranjan Ganguli2

    CMES-Computer Modeling in Engineering & Sciences, Vol.81, No.1, pp. 1-34, 2011, DOI:10.3970/cmes.2011.081.001

    Abstract The preliminary wing design of a low speed, long endurance UAV is formulated as a two step optimization problem. The first step performs a single objective aerodynamic optimization and the second step involves a coupled dual objective aerodynamic and structural optimization. During the first step, airfoil geometry is optimized to get maximum endurance parameter at a 2D level with maximum thickness to chord ratio and maximum camber as design variables. Leading edge curvature, trailing edge radius, zero lift drag coefficient and zero lift moment coefficient are taken as constraints. Once the airfoil geometry is finalized,… More >

  • Open Access

    ARTICLE

    An Improved Unsupervised Image Segmentation Method Based on Multi-Objective Particle Swarm Optimization Clustering Algorithm

    Zhe Liu1,2,*, Bao Xiang1,3, Yuqing Song1, Hu Lu1, Qingfeng Liu1

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 451-461, 2019, DOI:10.32604/cmc.2019.04069

    Abstract Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm. More >

  • Open Access

    ARTICLE

    Multi-Disciplinary Optimization for Multi-Objective Uncertainty Design of Thin Walled Beams

    Fangyi Li1, Guangyao Li2,3, Guangyong Sun2, Zhen Luo4, Zheng Zhang2

    CMC-Computers, Materials & Continua, Vol.19, No.1, pp. 37-56, 2010, DOI:10.3970/cmc.2010.019.037

    Abstract The focus of this paper is concentrated on multi-disciplinary and multi-objective optimization for thin walled beam systems considering safety, normal mode, static loading-bearing and weight, in which the uncertainties of the parameters are described via intervals. The size and shape of the cross-section are treated as design parameters during optimization. Considering the lightweight and safety, the design problem is formulated with two individual objectives to measure structural weight and maximum energy absorption, respectively, constrained by the average force, normal mode and maximum stress. The optimization problem with uncertainties is further transformed into a deterministic optimization More >

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