• 경영과학
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• 제15권1호
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• pp.77-85
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• 1998

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solutions of a multiple objective linear program. Since the efficient set is in general a nonconvex set, problem (P) can be classified as a global optimization problem. Perhaps due to its inherent difficulty, relatively few attempts have been made to solve problem (P) in spite of the potential benefits which can be obtained by solving problem (P). The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.169.4-169
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• 2001

Conventional methods to solve the nonlinear programming problem range from augmented Lagrangian methods to sequential quadratic programming (SQP) methods. NPSOL, which is a SQP code, has been widely used to solve various optimization problems but is still subject to many numerical problems such as convergence to local optima, difficulties in initialization and in handling non-smooth cost functions. Recently, many evolutionary methods have been developed for constrained optimization. Among them, CEALM (Co-Evolutionary Augmented Lagrangian Method) shows excellent performance in the following aspects: global optimization capability, low sensitivity to the initial parameter guessing, and excellent constraint handling capability due to the benefit of the augmented Lagrangian function. This algorithm is ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.107.6-107
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• 2001

Genetic alogorithm(GA) is a well-known global optimization algorithm. However, as the searching bounds grow wider., performance of local optimization deteriorates. In this paper, we propose a hybrid algorithm which integrates the gradient algorithm and GA so as to reinforce the performance of local optimization. We apply this algorithm to the system identification of second order RLC circuit. Identification results show that the proposed algorithm gets the better and robust performance to find the exact values of RLC elements.

• Journal of applied mathematics & informatics
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• 제28권3_4호
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• pp.823-835
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• 2010

In this paper, A FSQP algorithm for degenerate inequality constraints optimization problems is proposed. At each iteration of the proposed algorithm, a feasible direction of descent is obtained by solving a quadratic programming subproblem. To overcome the Maratos effect, a higher-order correction direction is obtained by solving another quadratic programming subproblem. The algorithm is proved to be globally convergent and superlinearly convergent under some mild conditions. Finally, some preliminary numerical results are reported.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.241-244
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide resemblance between individuals and research optimum solutions by single point method in reconstructive research space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제5권2호
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• pp.137-147
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• 2001

Nonconvex Quadratic Optimization Problems (QOP) are solved approximately by SDP (semidefinite programming) relaxation and SOCP (second order cone programmming) relaxation. Nonconvex QOPs with special structures can be solved exactly by SDP and SOCP. We propose a method to formulate general nonconvex QOPs into the special form of the QOP, which can provide a way to find more accurate solutions. Numerical results are shown to illustrate advantages of the proposed method.

• 환경영향평가
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• 제6권2호
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• pp.93-102
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• 1997

Acid rain below pH 5.6 is responsible for 40% of annual precipitation in Korea and it is more serious especially in major cites. Because of that, it is urgent to make measures to reduce the emission of $SO_2$, one of the major air pollutants causing acid rain. The national total emission of $SO_2$ in 1994 was estimated as 1.6 million tons. The $SO_2$ emission in 2020, is expected to increase up to 3.2 million tons, about 2 times that of 1994 under Business-As-Usual scenario. We could take various $SO_2$ reduction measures such as installing desulfurization facilities, the supply of low-sulfur oil and clean fuel(LNG), energy savings, upgrading of production process. However, it is necessary to check the economic feasibility and the attainability to reduction target with a dynamic optimization mode, "Global 2100 Model". The cost-benefit analyses for the measures using the revised "Global 2100 Model" clearly revealed that the desulfurization facilities should be introduced to reduce the $SO_2$ concentration to 0.01 ppm with fuel substitution. If the introduction of desulfurization facilities is delayed, We can not attain the goal of Ministry of Environment before the year of 2012, even in the case that almost all the fuels would be substituted with LNG.

• 대한기계학회논문집A
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• 제24권6호
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

• 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.

• 융합신호처리학회논문지
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• 제9권2호
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• pp.129-134
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• 2008

본 논문에서는 적자생존 원리에 기반한 유전자 알고리즘을 이용하여 일차원 비선형 광자 결정 구조에 대해 분석하고, 광 트랜지스터로의 적용 가능성을 컴퓨터 시뮬레이션에 의해 증명한다. 이와 같은 형태의 최적 설계는 해석식이 필요한 steepest decent 최적 알고리즘과 달리 유전자 알고리즘은 탁월한 성능을 낼 수 있으며, 광 트랜지스터 뿐만 아니라 다른 광자 결정 광소자의 설계에 유용하게 적용될 수 있다. 또한, global minimum 최적해 부근에서 여러 가지의 해가 얻어지기 때문에 광 트랜지스터가 어떤 모양을 가져야 되는지 분석하는데 많은 도움을 주는 장점을 갖는다. 완전 광 필터 트랜지스터를 설계하기 위해 신경회로망 모델을 이용하여 초기 설계를 수행한 후, 유전자 알고리즘에 의해 최종적인 최적화 설계가 수행된다. 시뮬레이션으로부터 얻어진 일차원 광자 결정 트랜지스터의 스위칭 On/Off 비는 약 27dB 였다.