• 한국전산유체공학회지
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• 제14권4호
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• pp.31-40
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• 2009

Although optimization by sequentially refining metamodels is known to be computationally very efficient, the metamodel that can be used for this purpose is limited to Kriging method due to the difficulties related with sample points selections. The present study suggests a novel method for sequentially refining metamodels using Pareto Frontiers, which can be used independent of the type of metamodels. It is shown from the examples that the present method yields more accurate metamodels compared with full-factorial optimization and also guarantees global optimum irrespective of the initial conditions. Finally, in order to prove the generality of the present method, it is applied to a 2D transonic airfoil optimization problem, and the successful design results are obtained.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 가을 학술발표회논문집
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• pp.141-148
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• 2000

In system design, it is not always possible that all decision makers can cooperate fully and thus avoid conflict. They each control a specified subset of design variables and seek to minimize their own cost functions subject to their individual constraints. However, a system management team makes every effort to coordinate multiple disciplines and overcome such noncooperative environment. Although full cooperation is difficult to achieve, noncooperation also should be avoided as possible. Our approach is to predict the results of their cooperation and generate approximate Pareto set for their multiple objectives. The Pareto set can be obtained according to the degree of one's conceding coupling variables in the other's favor. We employ approximation concept for modelling this coordination and the mutiobjective genetic algorithm for exploring the coupling variable space for obtaining an approximate Pareto set. The approximation management concept is also used for improving the accuracy of the Pareto set. The exploration for the coupling variable space is more efficient because of its smaller dimension than the design variable space. Also, our approach doesn't force the disciplines to change their own way of running analysis and synthesis tools. Since the decision making process is not sequential, the required time can be reduced comparing to the existing multidisciplinary optimization techniques. This approach is applied to some mathematical examples and structural optimization problems.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.207-212
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• 2008

This paper presents a robust and computationally efficient optimal design algorithm for electromagnetic devices by combining an adaptive response surface approximation of the objective function and($1+{\lambda}$) evolution strategy. In the adaptive response surface approximation, the design space is successively reduced with the iteration, and Pareto-optimal sampling points are generated by using Latin hypercube design with the Max Distance and Min Distance criteria. The proposed algorithm is applied to an analytic example and TEAM problem 22, and its robustness and computational efficiency are investigated.

• Journal of Computational Design and Engineering
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• 제1권3호
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• pp.187-193
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• 2014

One of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture the Pareto Front, approximate criteria (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to the springback and failure criteria. To optimize these two criteria, a global optimization algorithm was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria shape optimization of highly non-linear mechanical systems.

• Journal of the Korean Statistical Society
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• 제28권1호
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• pp.57-72
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• 1999

Falk(1994) showed that the asymptotic efficiency of the Pickands estimator of the extreme value index $\beta$ can considerably be improved by a simple convex combination. In this paper we propose an alternative estimator of $\beta$ which is as asymptotically efficient as the optimal convex combination of the Pickands estimators but has a better finite-sample performance. We prove consistency and asymptotic normality of the proposed estimator. Monte Carlo simulations are conducted to compare the finite-sample performances of the proposed estimator and the optimal convex combination estimator.

• 융합신호처리학회논문지
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• 제9권1호
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• pp.39-45
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• 2008

WDM 네트워크는 높은 전송속도와 낮은 지연시간으로 메트로폴리탄 네트워크뿐만 아니라 최근 기가비트 이더넷 등을 이용하여 근거리 망에서도 많은 연구가 진행되어 왔다. 네트워크의 성능은 네트워크 구조의 파라미터 값들과 사용되는 Medium Access Control 프로토콜의 파라미터 값들에 많이 의존한다. 또한 네트워크 효율성과 지연시간은 주로 상반된 관계를 보여 한쪽의 희생이 불가피 하였다. 네트워크를 효율적으로 운용하기 위해서는 효율성과 지연시간이라는 성능의 최적값을 찾아야 상황에 맞게 운용할 수 있다. 본 논문에서는 Arrayed Waveguide Grating (AWG) 기반의 성형 WDM 네트워크상에서 효율성의 최대화와 지연시간의 최소화라는 두 개의 서로 상반된 목적 함수를 유전자 알고리즘 기반의 방법론을 이용하여 파레토 최적화 곡선이라는 최적의 값들을 찾아내었다. 이를 이용하여 구한 최적의 네트워크 구성을 위한 파라미터 값들과 MAC 프로토콜의 파라미터 값들을 이용하여 상황에 따른 최적의 네트워크 성능을 유추할 수 있게 되었다. 본 논문에 사용된 유전자 알고리즘을 이용한 최적화 방법은 이와 유사한 상반된 목적 함수를 갖는 네트워크의 성능을 최적화하는데 사용필 수 있을 것이다.

• 한국생산제조학회지
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• 제24권2호
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• pp.192-197
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• 2015

In many fields, the importance of reducing weight is increasing. A product should be designed such that it is profitable, by lowering costs and exhibiting better performance than other similar products. In this study, the mass and deflection of steel structures have to be reduced as objective functions under constraint conditions. To reduce computational analysis time, central composite design(CCD) and D-Optimal are used in design of experiments(DOE). The accuracy of approximate models is evaluated using the $R^2$ value. In this study, the objective functions are multiple, so the non-dominant sorting genetic algorithm(NSGA-II), which is highly efficient, is used for such a problem. In order to verify the validity of Pareto solutions, CAE results and Pareto solutions are compared.

• 산업경영시스템학회지
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• 제32권1호
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• pp.72-78
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• 2009

This paper considers a multiagent scheduling problem under public information where a machine is shared by multiple agents. Each agent has a local objective among the minimization of total completion time and the minimization of maximum. In this problem, it is assumed that scheduling information is public. Therefore an agent can access to complete information of other agents and pursue efficient schedules in a centralized manner. We propose an enumeration scheme to find Pareto optimal schedules and a multiobjective genetic algorithm as a heuristic approach. Experimental results indicate that the proposed genetic algorithm yields close-to Pareto optimal solution under a variety of experimental conditions.

• 대한산업공학회지
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• 제22권4호
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• 센서학회지
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• 제33권3호
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• pp.134-138
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• 2024

In this study, we propose a ternary D flip-flop using tristate ternary inverters for an energy-efficient ternary circuit design of sequential logic. The tristate ternary inverter is designed by adding the functionality of the transmission gate to a standard ternary inverter without an additional transistor. The proposed flip-flop uses 18.18% fewer transistors than conventional flip-flops do. To verify the advancement of the proposed circuit, we conducted an HSPICE simulation with CMOS 28 nm technology and 0.9 V supply voltage. The simulation results demonstrate that the proposed flip-flop is better than the conventional flip-flop in terms of energy efficiency. The power consumption and worst delay are improved by 11.34% and 28.22%, respectively. The power-delay product improved by 36.35%. The above simulation results show that the proposed design can expand the Pareto frontier of a ternary flip-flop in terms of energy consumption. We expect that the proposed ternary flip-flop will contribute to the development of energy-efficient sensor systems, such as ternary successive approximation register analog-to-digital converters.