• 한국산학기술학회논문지
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• 제19권1호
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• pp.1-11
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• 2018

다양한 공학분야의 최적화 문제를 해결하기 위해 적절한 매개변수를 설정하기란 번거로운 작업이며, 매개변수 민감도 분석을 통해 적절한 매개변수를 설정하더라도 설정된 매개변수가 모든 문제에 적절한지 판단하기에는 한계가 있다. 이러한 이유로 매개변수를 문제에 따라 적절하게 설정하는 매개변수 자동검보정 (Self-adaptive) 화음탐색 알고리즘이 개발되고 발전하고 있다. 본 연구에서는 지금까지 개발된 자가적응형 하모니서치를 조사하고 그의 특성을 해탐색, 설정 매개변수, 적용성 등으로 구분하였으며, 이 중 매개변수 설정의 번거로움을 없애고, 적절한 매개변수 설정을 통해 해의 성능 향상을 위해 개발 된 6 가지 자가적응형 화음탐색 알고리즘을 선택하여 비교 분석을 수행하였다. 최적화 결과의 객관적인 비교를 위해 대표적인 수학적, 공학적 최적화 문제를 모두 적용 하였고, 다양한 성능 지수 (Performance index)를 사용하여 각 알고리즘의 성능을 정량적으로 비교하였다. 이것은 향후 신규 최적화 알고리즘을 개발하거나 해 탐색의 성능을 향상시키는 연구에 도움이 될 것으로 기대된다.

• 제어로봇시스템학회논문지
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• 제22권3호
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• pp.199-203
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• 2016

A brain-computer interface or BCI provides an alternative method for acting on the world. Brain signals can be recorded from the electrical activity along the scalp using an electrode cap. By analyzing the EEG, it is possible to determine whether a person is thinking about his/her hand or foot movement and this information can be transferred to a machine and then translated into commands. However, we do not know which information relates to motor imagery and which channel is good for extracting features. A general approach is to use all electronic channels to analyze the EEG signals, but this causes many problems, such as overfitting and problems removing noisy and artificial signals. To overcome these problems, in this paper we used a new optimization method called the Fruit Fly optimization algorithm (FOA) to select the best channels and then combine them with CSP method to extract features to improve the classification accuracy by linear discriminant analysis. We also used particle swarm optimization (PSO) and a genetic algorithm (GA) to select the optimal EEG channel and compared the performance with that of the FOA algorithm. The results show that for some subjects, the FOA algorithm is a better method for selecting the optimal EEG channel in a short time.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.216-221
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• 2008

Shape design optimization for linear elasticity problem is performed using isogeometric analysis method. In many design optimization problems for real engineering models, initial raw data usually comes from CAD modeler. Then designer should convert this CAD data into finite element mesh data because conventional design optimization tools are generally based on finite element analysis. During this conversion there is some numerical error due to a geometry approximation, which causes accuracy problems in not only response analysis but also design sensitivity analysis. As a remedy of this phenomenon, the isogeometric analysis method is one of the promising approaches of shape design optimization. The main idea of isogeometric analysis is that the basis functions used in analysis is exactly same as ones which represent the geometry, and this geometrically exact model can be used shape sensitivity analysis and design optimization as well. In shape design sensitivity point of view, precise shape sensitivity is very essential for gradient-based optimization. In conventional finite element based optimization, higher order information such as normal vector and curvature term is inaccurate or even missing due to the use of linear interpolation functions. On the other hands, B-spline basis functions have sufficient continuity and their derivatives are smooth enough. Therefore normal vector and curvature terms can be exactly evaluated, which eventually yields precise optimal shapes. In this article, isogeometric analysis method is utilized for the shape design optimization. By virtue of B-spline basis function, an exact geometry can be handled without finite element meshes. Moreover, initial CAD data are used throughout the optimization process, including response analysis, shape sensitivity analysis, design parameterization and shape optimization, without subsequent communication with CAD description.

• 대한수학회논문집
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• 제28권3호
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• pp.597-602
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• 2013

In this paper, Mond-Weir type duality results for a uncertain multiobjective robust optimization problem are given under generalized invexity assumptions. Also, weak vector saddle-point theorems are obtained under convexity assumptions.

• 대한수학회지
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• 제40권2호
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• pp.287-305
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• 2003

Second order necessary optimality condition for properly efficient solutions of a twice differentiable vector optimization problem is given. We obtain a nonsmooth version of the second order necessary optimality condition for properly efficient solutions of a nondifferentiable vector optimization problem. Furthermore, we prove a second order necessary optimality condition for weakly efficient solutions of a nondifferentiable vector optimization problem.

• 한국전산구조공학회논문집
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• 제28권4호
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• pp.425-431
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• 2015

본 논문에서는 균열 진전문제에 대하여 페리다이나믹스 이론을 이용하여 설계민감도 해석 및 구조 최적설계를 수행하였다. 페리다이나믹스는 해의 불연속성을 다루기 어려웠던 기존의 연속체 이론에 비해 균열 진전문제와 같은 불연속성을 가지는 문제를 자연스럽게 표현할 수 있다는 장점을 가지고 있다. 최적설계를 진행하기 위하여 애조인 변수법으로 설계민감도를 유도하였다. 특히 균열이 진전되더라도 애조인 변수법으로 계산된 변위장과 변형에너지에 대한 설계민감도 값은 유한차분법과 비교하여 매우 정확하고 효율적임을 보였다. 이를 바탕으로 간단한 인장응력 하의 균열진전 문제에 대하여 균열의 분기가 발생하는 위치를 조절하기 위하여 정해진 시간구간에서 변형에너지 값을 줄이는 방향으로 최적설계를 수행하였다. 최적의 재료분포로 해석을 수행한 결과 균열의 분기점을 늦출수 있음을 확인하였다.

• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.

• 한국산업정보학회논문지
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• 제27권1호
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• pp.37-48
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• 2022

본 연구에서는 공학설계 최적화 문제 해결을 위한 혼합 메타휴리스틱(Hybrid Meta-heuristic) 접근법을 제안된다. 공학 설계 최적화 문제는 다양한 형태의 변수를 가지며, 복잡한 제약조건들하에서 그 최적해를 구하는 문제로 이미 많은 기존 연구들을 통해 다양한 접근법들이 개발되어져 왔다. 하지만 그 효율성은 아직까지 크게 개선되지 못하고 있는 실정이다. 따라서 본 연구에서는 이러한 효율성을 개선하기 위한 새로운 접근법을 제안한다. 제안된 혼합 메타휴리스틱 접근법은 탐색 공간에 대한 전역적 탐색을 위해 유전알고리즘(Genetic Algorithm: GA) 접근법, 지역적 탐색을 위해 변동이웃탐색(Variable Neighborhood Search: VNS) 접근법과 언덕오르기(Hill Climbing: HC) 접근법을 혼합(GA-VNS-HC)하였다. 사례 연구에서는 다양한 형태의 공학설계 최적화 문제를 이용하여 본 연구에서 제안한 GA-VNS-HC 접근법의 우수성을 입증하였다.

• Structural Engineering and Mechanics
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• 제7권4호
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• pp.401-412
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• 1999

The purpose of this paper is to show how the Evolutionary Structural Optimization (ESO) algorithm developed by Xie and Steven can be extended to optimal design problems of thin shells subjected to thermal loading. This extension simply incorporates an evolutionary iterative process of thermoelastic thin shell finite element analysis. During the evolution process, lowly stressed material is gradually eliminated from the structure. This paper presents a number of examples to demonstrate the capabilities of the ESO algorithm for solving topology optimization and thickness distribution problems of thermoelastic thin shells.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 봄 학술발표회 논문집
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• pp.127-134
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• 2004

In this paper, using an adjoint variable method, we develop a design sensitivity analysis (DSA) method applicable to heat conduction problems in steady state. Also, a topology design optimization method is developed using the developed DSA method. Design sensitivity expressions with respect to the thermal conductivity are derived. Since the already factorized system matrix is utilized to obtain the adjoint solution, the cost for the sensitivity computation is trivial. For the topology design optimization, the design variables are parameterized into normalized bulk material densities. The objective function and constraint are the thermal compliance of structures and allowable material volume, respectively. Through several numerical examples, the developed DSA method is verified to yield very accurate sensitivity results compared with finite difference ones, requiring less than 0.3% of CPU time far the finite differencing. Also, the topology optimization yields physical meaningful results.