• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.643-653
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• 2017

The paper presents controller optimization algorithm for a 12-pulse voltage source converter (VSC) based high voltage direct current (HVDC) system. To get an optimum algorithm, three methods namely conventional-Zeigler-Nichols, linear-golden section search (GSS) and stochastic-particle swarm optimization (PSO) are applied to control of 12 pulse VSC based HVDC system and simulation results are presented to show the best among the three. The performance results are obtained under various dynamic conditions such as load perturbation, non-linear load condition, and voltage sag, tapped load fault at points-of-common coupling (PCC) and single-line-to ground (SLG) fault at input AC mains. The conventional GSS and PSO algorithm are modified to enhance their performances under dynamic conditions. The results of this study show that modified particle swarm optimization provides the best results in terms of quick response to the dynamic conditions as compared to other optimization methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1218-1228
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• 1997

The move limit strategy is used to avoid the excessive approximation in the structural optimization. The size of move limit has been obtained by engineering experience. Recently, efforts based on analytic methods are performed by some researchers. These methods still have problems, such as prematurity or oscillation of the move limit size. The existing methods usually control the bound of design variables based on the magnitude. Thus, they can not properly handle the configuration variables based on the geometry in the configuration optimization. In this research, the size of move limit is calculated based on the accuracy of approximation. The method is coded and applied to the two-point reciprocal quadratic approximation method. The efficiency is evaluated through examples.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.492-498
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• 2009

The placement of the UPFC is the major concern to ensure the full potential of utilization in the transmission network. Voltage stability enhancement with the optimal placement of UPFC using stability index such as modal analysis, Voltage Phasor method is made and the loss minimization including UPFC is formulated as an optimization problem. This paper proposes particle swarm optimization for the exact real power loss minimization including UPFC. The implementation of loss minimization for the optimal location of UPFC was tested with IEEE-14 and IEEE-57 bus system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.1
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• pp.158-166
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• 2003

The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

• Journal of Magnetics
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• v.19 no.4
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• pp.385-392
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• 2014

Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.109-113
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• 2010

We present the minimum weight optimum design of cross sectional for frame structures subject to natural frequency. The optimum design in this paper employ discrete and continuous design variables and Genetic Algorithms. In this paper, Genetic Algorithms is used in optimization process, and be used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process. For 1-Bay 2-Story frame structure, in examples, continuous and discrete design variables are used, and W-section (No.1~No.64), from AISC, discrete data are used in discrete optimization. In this case, Exhaustive search are used for finding global optimum. Continuous variables are used for 1-Bay 7-Story frame structure. Two typical frame structure optimization examples are employed to demonstrate the availability of Genetic Algorithms for solving minimum weight optimum of frame structures with fundamental and multi frequency.

• Structural Engineering and Mechanics
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• v.52 no.4
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• pp.739-753
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• 2014

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

• Structural Engineering and Mechanics
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• v.52 no.4
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• pp.755-765
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• 2014

In this present work, Artificial Bee Colony Algorithm (ABCA) is used to optimize the stacking sequences of simply supported antisymmetric laminated composite plates with criticial buckling load as the objective functions. The fibre orientations of the layers are selected as the optimization design variables with the aim to find the optimal laminated plates. In order to perform the optimization based on the ABCA, a special code is written in MATLAB software environment. Several numerical examples are presented to illustrate this optimization algorithm for different plate aspect ratios, number of layers and load ratios.

• Computational Structural Engineering : An International Journal
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• v.1 no.1
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• pp.21-29
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• 2001

The general topology optimization can be considered as optimal material distribution. Such an approach can be unstable, unless composite materials are introduced. In this research, a nodal volume fraction method is used to obtain the optimum topology of continuum structures. This method is conducted from a composite material model composed of isotropic matter and spherical void. Because the appearance of the chessboard patterns makes the interpretation of the optimal material layout very difficult, this method contains a chessboard prevention strategy. In this research, several topology optimization problems are presented to demonstrate the validity of the present method and the recursive quadratic programming algorithm is used to solve the topology optimization problems.