• 한국CDE학회논문집
• /
• 제2권4호
• /
• pp.215-221
• /
• 1997

A new approach in solving design centering problem is presented. Like most stochastic optimization problems, optimal design centering problems have intrinsic difficulties in multivariate intergration of probability density functions. In order to avoid to avoid those difficulties, genetic algorithm and very coarse Monte Carlo simulation are used in this research. The new algorithm performs robustly while producing improved yields. This result implies that the combination of robust optimization methods and approximated simulation schemes would give promising ways for many stochastic optimizations which are inappropriate for mathematical programming.

• East Asian mathematical journal
• /
• 제34권1호
• /
• pp.1-16
• /
• 2018

We study an optimization problem for hyperbolic absolute risk aversion (HARA) utility function under two-factor Heston's stochastic volatility model. It is not possible to obtain an explicit solution because our financial market model is complicated. However, by using asymptotic analysis technique, we find the explicit forms of the approximations of the optimal value function and the optimal strategy for HARA utility function.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 하계학술대회 논문집 B
• /
• pp.699-701
• /
• 1997

The simulated annealing(SA) algorithm is a stochastic strategy for search of the ground state and a powerful tool for optimization, based on the annealing process used for the crystallization in physical systems. It's main disadvantage is the long convergence time. Therefore, this paper proposes a stochastic algorithm combined with conventional deterministic optimization method to reduce the computation time, which is called SDS(Stochastic-Deterministic-Stochastic) method.

• 한국시뮬레이션학회논문지
• /
• 제32권1호
• /
• pp.23-34
• /
• 2023

본 논문의 목적은 이항출력 실험을 이용할 경우에 확률적 전역 최적화 방법론들을 검토하고 알고리즘들간의 성능을 비교하기 위한 것이다. 모 성공확률은 알수 없고 확률적 특성을 갖기 때문에 확률적 전역 최적화 방법론에서는 모 성공확률 대신 성공확률의 추정치를 이용한다. 언덕오르기 알고리즘 , 단순랜덤탐색, 랜덤재출발 랜덤탐색, 랜덤 최적화, 담금질 기법 및 군집기반의 알고리즘인 입자 군집 최적화 알고리즘을 확률적 전역 최적화 알고리즘으로 사용하였다. 알고리즘의 비교를 위하여 두가지 테스트 함수(하나는 단봉이고 나머지는 다봉임)가 제안되었고 몬테카를로 시뮬레이션을 이용하여 알고리즘의 성능을 평가하였다. 단순 테스트 함수에 대하여는 모든 알고리즘이 유사한 성능을 보이고 있다. 복잡한 다봉의 테스트 함수에 대하여는 랜덤재출발 랜덤최적화, 담금질 기법과 군집 기반의 입자군집 알고리즘이 훨씬 더 좋은 성능을 보임을 알 수 있다.

• 한국지반신소재학회논문집
• /
• 제21권3호
• /
• pp.1-10
• /
• 2022

본 논문에서는 지반 구조물의 재료 물성치에 대한 추계장 세트를 이용하여 재료 물성치의 공간적 임의성 및 확률 특성을 역추정한다. 이러한 추정된 재료 물성치의 확률분포 및 확률특성을 이용하여 구조 형상에 대한 위상 최적화를 수행하고, 기존의 결정론적 위상 최적화 결과와 비교한다. 재료 물성치에 대한 한 세트의 추계장들을 생성하고, 각 추계장에서 재료 물성치의 공간적 임의성을 모사한다. 각 추계장에서 재료 물성치의 부분값들을 이용하여 실제 재료 물성치의 확률분포와 확률 특성을 추정한다. 추정된 실제 재료 물성치의 확률특성을 추계장 세트의 확률 특성과 비교한다. 또한, 임의성을 가진 재료탄성계수를 가지는 지반구조물의 최적화 응답변화도와 임의성이 없는 재료탄성계수를 가지는 지반구조의 응답변화도를 비교한다. 따라서, 실제 재료 물성치의 공간적 임의성을 고려한 정량화된 확률론적 위상 최적화 결과를 얻을 수 있다.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제49권4호
• /
• pp.219-225
• /
• 2000

This paper presents the shape optimization considering the uncertainty of design variable to find robust optimal solution that has insensitive performance to its change of design variable. Stochastic finite element method (SFEM) is used to treat input data as stochastic variables. It is method that the potential values are series form for the expectation and small variation. Using correlation function of their variables, the statistics of output obtained form the input data distributed. From this, design considering uncertainty of design variables.

• Industrial Engineering and Management Systems
• /
• 제16권1호
• /
• pp.44-51
• /
• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• 한국생산제조학회지
• /
• 제9권1호
• /
• pp.45-52
• /
• 2000

The purpose of this research was developing an integrated way to solve two typical tolerance optimization problem i.e. optimal tolerance allotment and design centering. A new problem definition design centering-tolerance allotment problem (DCTA) was proposed here for the first time and solved. Genetic algorithm and coarse Monte Carlo simulation were used to solve the stochastic optimization problem. Optimal costs were compared with the costs from the previous optimization strategies Significant cost reductions were achieved by DCTA scheme.

• Structural Engineering and Mechanics
• /
• 제36권1호
• /
• pp.79-94
• /
• 2010

This study shows how uncertainties of data like material properties quantitatively have an influence on structural topology optimization results for dynamic problems, here such as both optimal topology and shape. In general, the data uncertainties may result in uncertainties of structural behaviors like deflection or stress in structural analyses. Therefore optimization solutions naturally depend on the uncertainties in structural behaviors, since structural behaviors estimated by the structural analysis method like FEM need to execute optimization procedures. In order to quantitatively estimate the effect of data uncertainties on topology optimization solutions of dynamic problems, a so-called interval analysis is utilized in this study, and it is a well-known non-stochastic approach for uncertainty estimate. Topology optimization is realized by using a typical SIMP method, and for dynamic problems the optimization seeks to maximize the first-order eigenfrequency subject to a given material limit like a volume. Numerical applications topologically optimizing dynamic wall structures with varied supports are studied to verify the non-stochastic interval analysis is also suitable to estimate topology optimization results with dynamic problems.

• ETRI Journal
• /
• 제46권3호
• /
• pp.367-378
• /
• 2024

We propose a number-theory-based quantized mathematical optimization scheme for various NP-hard and similar problems. Conventional global optimization schemes, such as simulated and quantum annealing, assume stochastic properties that require multiple attempts. Although our quantization-based optimization proposal also depends on stochastic features (i.e., the white-noise hypothesis), it provides a more reliable optimization performance. Our numerical analysis equates quantization-based optimization to quantum annealing, and its quantization property effectively provides global optimization by decreasing the measure of the level sets associated with the objective function. Consequently, the proposed combinatorial optimization method allows the removal of the acceptance probability used in conventional heuristic algorithms to provide a more effective optimization. Numerical experiments show that the proposed algorithm determines the global optimum in less operational time than conventional schemes.