• 제목/요약/키워드: Sequential Optimization

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Comprehensive studies of Grassmann manifold optimization and sequential candidate set algorithm in a principal fitted component model

  • Chaeyoung, Lee;Jae Keun, Yoo
    • Communications for Statistical Applications and Methods
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    • 제29권6호
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    • pp.721-733
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    • 2022
  • In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

근사 최적설계를 위한 순차 설계영역에 관한 연구 (A Study on the Sequential Design Domain for the Approximate Optimum Design)

  • 김정진;이진식;임오강
    • 한국전산구조공학회논문집
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    • 제14권3호
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    • pp.339-348
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    • 2001
  • 상용 구조해석 프로그램을 이용한 구조물의 최적설계에서는 최적화 프로그램과 구조해석 프로그램의 연결 및 두 프로그램 사이의 데이터 교환이 용이하지 못하다. 최근 많은 구조물 설계자들은 근사 최적화 기법을 이용하여 이와 같은 문제들을 해결하고 있다. 일반적으로 최적실계 문제의 설계변수에 대한 설계영역은 아주 작은 값에서 아주 큰 값으로 범위가 정해진다. 이렇게 넓은 설계영역에서 생성된 시스템 응답 근사식의 정확도는 떨어지게 되며, 이는 근사 최적해에 지배적인 영향을 미친다. 따라서, 본 연구의 목적은 넓은 설계영역에서 정확도가 높은 근사식 생성을 위한 순차 설계영역법 개발에 있다. 순차 설계영역에서의 근사식은 반응표면법을 이용하여 구성하고, 반응표면법에 필요한 실험방법으로는 직교 배열표를 사용한다. 본 연구에서는 순차 설계영역법의 신뢰도 검증을 위하여 3부재 및 10부재 트러스 구조물을 수치예제로 선정한다.

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크리깅 모델을 이용한 순차적 근사최적화 (Sequential Approximate Optimization Using Kriging Metamodels)

  • 신용식;이용빈;류제선;최동훈
    • 대한기계학회논문집A
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    • 제29권9호
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    • pp.1199-1208
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    • 2005
  • Nowadays, it is performed actively to optimize by using an approximate model. This is called the approximate optimization. In addition, the sequential approximate optimization (SAO) is the repetitive method to find an optimum by considering the convergence of an approximate optimum. In some recent studies, it is proposed to increase the fidelity of approximate models by applying the sequential sampling. However, because the accuracy and efficiency of an approximate model is directly connected with the design area and the termination criteria are not clear, sequential sampling method has the disadvantages that could support an unreasonable approximate optimum. In this study, the SAO is executed by using trust region, Kriging model and Optimal Latin Hypercube design (OLHD). Trust region is used to guarantee the convergence and Kriging model and OLHD are suitable for computer experiment. finally, this SAO method is applied to various optimization problems of highly nonlinear mathematical functions. As a result, each approximate optimum is acquired and the accuracy and efficiency of this method is verified by comparing with the result by established method.

Augmented D-Optimal Design for Effective Response Surface Modeling and Optimization

  • Kim, Min-Soo;Hong, Kyung-Jin;Park, Dong-Hoon
    • Journal of Mechanical Science and Technology
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    • 제16권2호
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    • pp.203-210
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    • 2002
  • For effective response surface modeling during sequential approximate optimization (SAO), the normalized and the augmented D-optimality criteria are presented. The normalized D-optimality criterion uses the normalized Fisher information matrix by its diagonal terms in order to obtain a balance among the linear-order and higher-order terms. Then, it is augmented to directly include other experimental designs or the pre-sampled designs. This augmentation enables the trust region managed sequential approximate optimization to directly use the pre-sampled designs in the overlapped trust regions in constructing the new response surface models. In order to show the effectiveness of the normalized and the augmented D-optimality criteria, following two comparisons are performed. First, the information surface of the normalized D-optimal design is compared with those of the original D-optimal design. Second, a trust-region managed sequential approximate optimizer having three D-optimal designs is developed and three design problems are solved. These comparisons show that the normalized D-optimal design gives more rotatable designs than the original D-optimal design, and the augmented D-optimal design can reduce the number of analyses by 30% - 40% than the original D-optimal design.

SEQUENTIAL MINIMAL OPTIMIZATION WITH RANDOM FOREST ALGORITHM (SMORF) USING TWITTER CLASSIFICATION TECHNIQUES

  • J.Uma;K.Prabha
    • International Journal of Computer Science & Network Security
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    • 제23권4호
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    • pp.116-122
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    • 2023
  • Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

순차적 실험계획법을 이용한 위상 최적 설계 (Sequential Design of Experiment Based Topology Optimization)

  • 송치오;박순옥;유정훈
    • 정보저장시스템학회논문집
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    • 제3권4호
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    • pp.178-182
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    • 2007
  • Topology optimization methods are classified into two methods such as the density method and the homogenization method. Those methods need to consider relationships between the material property and the density of each element in a design domain, the relaxation of the design space, etc. However, it is hard to apply on some cases due to the complexity to compose the design objective and its sensitivity analysis. In this paper, a modified topology optimization is proposed to assist designers who do not have mathematical or theoretical background of the topology optimization. In this study, optimal topology of structures can be achieved by the sequential design of experiment (DOE) and the sensitivity analysis. We conducted the DOE with an orthogonal array and the sensitivity analysis of design variables to determine sensitive variables used for connectivity between elements. The modified topology optimization method has advantages such as freedom from penalizing intermediate values and easy application with basic DOE concept.

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응답량 재사용을 통한 순차 근사최적설계 (A Sequential Approximate Optimization Technique Using the Previous Response Values)

  • 황태경;최은호;임오강
    • 대한기계학회논문집A
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    • 제29권1호
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    • pp.45-52
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    • 2005
  • A general approximate optimization technique by sequential design domain(SDD) did not save response values for getting an approximate function in each step. It has a disadvantage at aspect of an expense. In this paper, previous response values are recycled for constructing an approximate function. For this reason, approximation function is more accurate. Accordingly, even if we did not determine move limit, a system is converged to the optimal design. Size and shape optimization using approximate optimization technique is carried out with SDD. Algorithm executing Pro/Engineer and ANSYS are automatically adopted in the approximate optimization program by SDD. Convergence criterion is defined such that optimal point must be located within SDD during the three steps. The PLBA(Pshenichny-Lim-Belegundu-Arora) algorithm is used to solve approximate optimization problems. This algorithm uses the second-order information in the direction finding problem and uses the active set strategy.

순차적 근사최적화 기법을 이용한 방열판 최적설계 (Optimal Design of a Heat Sink using the Sequential Approximate Optimization Algorithm)

  • 박경우;최동훈
    • 설비공학논문집
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    • 제16권12호
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    • pp.1156-1166
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    • 2004
  • The shape of plate-fin type heat sink is numerically optimized to acquire the minimum pressure drop under the required temperature rise. In constrained nonlinear optimization problems of thermal/fluid systems, three fundamental difficulties such as high computational cost for function evaluations (i.e., pressure drop and thermal resistance), the absence of design sensitivity information, and the occurrence of numerical noise are commonly confronted. Thus, a sequential approximate optimization (SAO) algorithm has been introduced because it is very hard to obtain the optimal solutions of fluid/thermal systems by means of gradient-based optimization techniques. In this study, the progressive quadratic response surface method (PQRSM) based on the trust region algorithm, which is one of sequential approximate optimization algorithms, is used for optimization and the heat sink is optimized by combining it with the computational fluid dynamics (CFD).

순차 컨벡스 프로그래밍을 이용한 충돌각 제어 비행궤적 최적화 (Trajectory Optimization for Impact Angle Control based on Sequential Convex Programming)

  • 권혁훈;신효섭;김윤환;이동희
    • 전기학회논문지
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    • 제68권1호
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    • pp.159-166
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    • 2019
  • Due to the various engagement situations, it is very difficult to generate the optimal trajectory with several constraints. This paper investigates the sequential convex programming for the impact angle control with the additional constraint of altitude limit. Recently, the SOCP(Second-Order Cone Programming), which is one area of the convex optimization, is widely used to solve variable optimal problems because it is robust to initial values, and resolves problems quickly and reliably. The trajectory optimization problem is reconstructed as convex optimization problem using appropriate linearization and discretization. Finally, simulation results are compared with analytic result and nonlinear optimization result for verification.