Dong Qiu, Rongwen Dong, Shuqiao Chen, Andi Li

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 147-150, 2018, DOI:10.1080/10798587.2017.1327153

Abstract In this paper, we research the optimization problems with multiple Z-number valued objectives. First, we convert Z-numbers to classical fuzzy numbers to simplify the calculation. A new dominance relationship of two fuzzy numbers based on the lower limit of the possibility degree is proposed. Then according to this dominance relationship, we present a multi-objective evolutionary algorithm to solve the optimization problems. Finally, a simple example is used to demonstrate the validity of the suggested algorithm. More >

Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, 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 (defective or non-defective). (2) we… More >

Ahmed Y. Hamed¹, Monagi H. Alkinani², M. R. Hassan^{3, *}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1159-1173, 2020, DOI:10.32604/cmc.2020.09176

Abstract In the distributed networks, many applications send information from a source node to multiple destination nodes. To support these applications requirements, the paper presents a multi-objective algorithm based on ant colonies to construct a multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weight (cost, delay and hop) of the multicast tree. Experimental results prove the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, it is able to find a better solution with fast convergence speed and high reliability. More >

Fangyi Li^1,2, 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 the inner loop to evaluate… More >

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 of the objective functions as… More >

Z. Q. Zhu¹, Z. Liu¹, X. L. Wang¹, R. X. Yu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.6, No.6, pp. 567-576, 2004, DOI:10.3970/cmes.2004.006.567

Abstract To extend an available mono-objective optimization method to multi-objective/multi-disciplinary optimization, the construction of a suitable integral objective function (in gradient based deterministic method-DM) or fitness function (in genetic algorithm-GA) is important. An auto-adjusting weighted object optimization (AWO) method in DM is suggested to improve the available weighted sum method (linear combined weighted object optimizationLWO method). Two formulae of fitness function in GA are suggested for two kinds of design problems. Flow field solution is obtained by solving Euler equations. Electromagnetic field solution is obtained by solving Maxwell equations. Bi-disciplinary optimization computation is carried out by coupling these two solutions with… More >

Zichun Yang^1,2, Maolin Peng^1,3,4, 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 of mean value and standard… More >

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 attached to a cylinder in… More >

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 using available wind tunnel data.… More >

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 flow characteristics important to the… More >