• Structural Engineering and Mechanics
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• 제73권4호
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• pp.397-405
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• 2020

In this study size optimization of large-scale dome structures with dynamic constraints is presented. In the optimal design of these structure, the Jaya algorithm is used to find minimal size of design variables. The design variables are the cross-sectional areas of the steel truss bar elements. To take into account the constraints which are the first five natural frequencies of the structures, the finite element analysis is coded in Matlab programs using eigen values of the stiffness matrix of the dome structures. The Jaya algorithm and the finite elements codes are combined by the help of the Matlab - GUI (Graphical User Interface) programming to carry out the optimization process for the dome structures. To show the efficiency and the advances of the Jaya algorithm, 1180 bar dome structure and the 1410 bar dome structure were tested by taking into the frequency constraints. The optimal results obtained by the proposed algorithm are compared with those given in the literature to demonstrate the performance of the Jaya algorithm. At the end of the study, it is concluded that the proposed algorithm can be effectively used in the optimal design of large-scale dome structures.

• 전기학회논문지
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• 제59권5호
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• pp.897-903
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• 2010

This paper presents a response surface method(RSM) with Latin Hypercube Sampling strategy, which is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed LHS algorithm consists of the multi-objective Pareto optimization and (1+1) evolution strategy. The algorithm is compared with the uniform sampling point method in view points of computing time and convergence. In order to verify the developed algorithm, a 6 MW BLDC motor is simulated with 4 design parameters (arc length and 3 variables for magnet) and 4 constraints for minimizing of the cogging torque. The optimization procedure has two stages; the fist is to optimize the arc length of the PM and the second is to optimize the magnet pole shape by using the proposed hybrid algorithm. At the 3rd iteration, an optimal point is obtained, and the cogging torque of the optimized shape is converged to about 14% of the initial one. It means that 3 iterations aregood enough to obtain the optimal design parameters in the program.

• Journal of Communications and Networks
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• 제11권2호
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• pp.157-165
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• 2009

In this paper, we investigate a dynamic spectrum sharing problem for the centralized uplink cognitive radio networks using orthogonal frequency division multiple access. We formulate a large-scale joint rate and power allocation as an optimization problem under quality of service constraint for secondary users and interference constraint for primary users. We also suggest admission control to nd a feasible solution to the optimization problem. To implement the resource allocation on a large-scale, we introduce a notion of using the conservative factors $\alpha$ and $\beta$ depending on the outage and violation probabilities. Since estimating instantaneous channel gains is costly and requires high complexity, the proposed algorithm pursues a practical and implementation-friendly resource allocation. Simulation results demonstrate that the large-scale joint rate and power allocation incurs a slight loss in system throughput over the instantaneous one, but it achieves lower complexity with less sensitivity to variations in shadowing statistics.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.17-22
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• 2012

Lagrangian relaxation is the most widely adopted method for solving unit commitment (UC) problems. It consists of two steps: dual optimization and primal feasible solution construction. The dual optimization step is crucial in determining the overall performance of the solution. This paper intends to evaluate two dual optimization methods - one based on subgradient (SG) and the other based on the cutting plane. Large-scale UC problems with hundreds of thousands of variables and constraints have been generated for evaluation purposes. It is found that the evaluated SG method yields very promising results.

• 대한기계학회논문집A
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• 제31권11호
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• pp.1059-1068
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• 2007

Nonlinear response structural optimization is performed using equivalent loads (NROEL). Nonlinear response optimization is extremely cost because many nonlinear analyses are required. In NROEL, the external loads are transformed to the equivalent loads (EL) for linear static analysis and linear response optimization is carried out based on the EL in a cyclic manner until the convergence criteria are satisfied. EL is the load set which generates the same response field of linear analysis as that of nonlinear analysis. The primitive from of theory has been published. In this research, the theory is investigated with large scale example problems. Four examples are solved by using NROEL. Conventional optimization with sensitivity analysis using the finite difference method (FDM) is also applied to the same examples. Moreover, response surface optimization method is applied to the last two examples. The results of the optimizations are compared. In nonlinear response optimization of large scale problems, hundreds (or even thousands) of nonlinear analyses are expected to satisfy the convergence criteria. However, in nonlinear response optimization using equivalent loads, only tens of nonlinear analyses are required. The results are discussed and the usefulness of NROEL is presented.

• 한국정밀공학회지
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• 제20권11호
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• pp.117-126
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• 2003

GA(genetic algorithm) has a powerful searching ability and is comparatively easy to use and to apply as well. By that reason, GA is in the spotlight these days as an optimization skill for mechanical systems.$^1$However, GA has a low efficiency caused by a huge amount of repetitive computation and an inefficiency that GA meanders near the optimum. It also can be shown a phenomenon such as genetic drifting which converges to a wrong solution.$^{8}$ These defects are the reasons why GA is not widdy applied to real world problems. However, the low efficiency problem and the meandering problem of GA can be overcomed by introducing parallel computation$^{7}$ and gray code$^4$, respectively. Standard GA(SGA)$^{9}$ works fine on small to medium scale problems. However, SGA done not work well for large-scale problems. Large-scale problems with more than 500-bit of sere's have never been tested and published in papers. In the result of using the SGA, the powerful searching ability of SGA doesn't have no effect on optimizing the problem that has 96 design valuables and 1536 bits of gene's length. So it converges to a solution which is not considered as a global optimum. Therefore, this study proposes ExpGA(experience GA) which is a new genetic algorithm made by applying a new probability parameter called by the experience value. Furthermore, this study finds the solution throughout the whole field searching, with applying ExpGA which is a optimization technique for the structure having genetic drifting by the standard GA and not making a optimization close to the best fitted value. In addition to them, this study also makes a research about the possibility of GA as a optimization technique of large-scale design variable problems.

• Journal of KIEE
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• 제11권1호
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• pp.8-13
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• 2001

In this brief, the design method of robust non-fragile decentralized controllers for uncertain large-scale interconnected systems is proposed. Based on Lyapunov second method, a sufficient condition for asymtotic stability is derived in terms of a linear matrix inequality (LMI), and the measure of non-fragility in controller is presented. The solutions of the LMI can be easily obtained using efficient convex optimization techniques. A numerical example is given to illustrate the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.547-549
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• 1986

This paper deals with large scale distributed packet switching system which is modeled by state space form and optimizing routing algorithms and buffer size via a hierachical system optimization method, the interaction prediction method.

• Structural Engineering and Mechanics
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• 제46권5호
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• pp.735-753
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• 2013

The wind load is always the dominant load of cooling tower due to its large size, complex geometry and thin-wall structure. At present, when computing the wind-induced response of the large-scale cooling tower, the wind pressure distribution is obtained based on code regulations, wind tunnel test or computational fluid dynamic (CFD) analysis, and then is imposed on the tower structure. However, such method fails to consider the change of the wind load with the deformation of cooling tower, which may result in error of the wind load. In this paper, the analysis of the large cooling tower based on the iterative method for wind pressure is studied, in which the advantages of CFD and finite element method (FEM) are combined in order to improve the accuracy. The comparative study of the results obtained from the code regulations and iterative method is conducted. The results show that with the increase of the mean wind speed, the difference between the methods becomes bigger. On the other hand, based on the design of experiment (DOE), an approximate model is built for the optimal design of the large-scale cooling tower by a two-level optimization strategy, which makes use of code-based design method and the proposed iterative method. The results of the numerical example demonstrate the feasibility and efficiency of the proposed method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
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• pp.217.1-217.1
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• 2005