• 한국강구조학회 논문집
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• 제16권1호통권68호
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• pp.123-134
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• 2004

본 연구에서는 효율적인 형상최적화를 위해 다단계 분할기법으로 트러스 구조물의 형상 최적화를 시도하였다. 1단계에서는 단면적을 설계변수로 하여 중량, 또는 체적을 목적함수로 하고 다하중 재하조건 하의 거동제약조건과 부가적인 제약조건을 고려하여 비선형 최적화 문제를 형성한다. 이 비선형 계획문제를 축차 선형계획 문제로 변환하여 개선된 허용방향법으로 최적화하였다. 이때 필요한 도함수는 다른 연구와 달리 효율적이라고 알려진 거동공간법으로 구하였고, 최적화 과정 중 이를 이용하여 부재력를 근사화 함으로써 계산의 효율성을 높였다. 2단계에서는 형상 설계변수만을 고려한 무제약 최적화 문제로 형성한 후 일방향 탐사기법을 적용하여 형상을 최적화하였다. 이와 같이 구성된 본 연구의 알고리즘을 몇 가지 트러스 구조물에 적용하여 본 알고리즘의 적용성과 효율성 및 타당성을 증명하였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제9권5호
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• pp.499-508
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• 2017

This paper uses optimization techniques to obtain bow hull form of a 66,000 DWT bulk carrier in calm water and in waves. Parametric modification functions of SAC and section shape of DLWL are used for hull form variation. Multi-objective functions are applied to minimize the wave-making resistance in calm water and added resistance in regular head wave of ${\lambda}/L=0.5$. WAVIS version 1.3 is used to obtain wave-making resistance. The modified Fujii and Takahashi's formula is applied to obtain the added resistance in short wave. The PSO algorithm is employed for the optimization technique. The resistance and motion characteristics in calm water and regular and irregular head waves of the three hull forms are compared. It has been shown that the optimal brings 13.2% reduction in the wave-making resistance and 13.8% reduction in the added resistance at ${\lambda}/L=0.5$; and the mean added resistance reduces by 9.5% at sea state 5.

• Computers and Concrete
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• 제21권4호
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• pp.441-449
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• 2018

FE models for complex or large-scaled structures that need detailed modeling of structural components are usually constructed using commercial analysis softwares. Updating of such FE model by conventional sensitivity-based methods is difficult since repeated computation for perturbed parameters and manual calculations are needed to obtain sensitivity matrix in each iteration. In this study, an FE model updating procedure avoiding such difficulties by using response surface (RS) method and a Pareto-based multiobjective optimization (MOO) was formulated and applied to FE models constructed with a commercial analysis package. The test building is a low-rise reinforced concrete building that has been seismically retrofitted. Dynamic properties of the building were extracted from vibration tests performed before and after the seismic retrofits, respectively. The elastic modulus of concrete and masonry, and spring constants for the expansion joint were updated. Two RS functions representing the errors in the natural frequencies and mode shape, respectively, were obtained and used as the objective functions for MOO. Among the Pareto solutions, the best compromise solution was determined using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) procedure. A similar task was performed for retrofitted building by taking the updating parameters as the stiffness of modified or added members. Obtained parameters of the existing building were reasonably comparable with the current code provisions. However, the stiffness of added concrete shear walls and steel section jacketed members were considerably lower than expectation. Such low values are seemingly because the bond between new and existing concrete was not as good as the monolithically casted members, even though they were connected by the anchoring bars.

• 한국구조물진단유지관리공학회 논문집
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• 제8권3호
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• pp.131-140
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• 2004

본 연구의 목적은 재료 및 기하학적 비선형을 고려한 개선된 소성힌지 해석을 이용한 평면 강골조 시스템의 최적설계알고리즘 개발이다. 개발된 알고리즘을 이용하여 기둥의 유효길이계수(K-factor) 산정없이 골조의 최적설계를 수행하였다. 본 연구에서 2개의 파리미터를 이용한 다단계 이산화 최적화기법이 개발되어 적용되었다. 목적함수는 구조물의 중량을, 제약조건은 임계하중, 사용성, 유연성등이 고려되었다. 예제를 통해 개발된 프로그램의 효율성, 적용성, 유용성등을 보였다.

• 대한조선학회논문집
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• 제44권2호
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• pp.139-147
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• 2007

The ultimate goal of structural design is to find the optimal design results which satisfies both safety and economy at the same time. Optimum design has been studied for the last several decades and is being studied. in this study, an optimum algorithm which is based on the genetic algorithm has been applied to the multi-object problem to obtain the optimum solutions which minimizes structural weight and construction cost of panel blocks in ship structures at the same time. Mathematical problems are dealt at first to justify the reliability of the present optimum algorithm. And then the present method has been applied to the panel block model which can be found in ship structures. From the present findings it has been seen that the present optimum algorithm can reasonably give the optimum design results.

• 제어로봇시스템학회논문지
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• 제15권3호
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• pp.280-285
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• 2009

This paper introduced an automatic generation method of falling motions for humanoid robots to minimize a damage. The proposed approach used a PGA based optimization technique to find a set of joint trajectories which minimize a damage of the falling over and down. Injection-migration PGA technique is introduced and compared with EMO and various migration topologies. To verify the proposed method, experiments for falling motions were executed for Sony QRIO robot in Webots simulation environments.

• 한국지진공학회논문집
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• 제7권5호
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• pp.93-102
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• 2003

이 연구에서는 지진하중을 받는 빌딩구조물에 대한 복합구조제어시스템의 최적설계방법을 제시한다. 복합구조제어시스템의 설계는 구조물의 부재뿐만 아니라 수동제어시스템 및 능동제어시스템의 용량 및 위치 최적화 과정으로 정의된다. 최적설계는 이 연구에서 제안된 다단계 목표계획법(Multi-Stage Goal Programming)을 이용하여 최적화문제를 정식화하고 목표갱신 유전자알고리즘(Goal-Updating Genetic Algorithm을 적용하여 합리적인 최적화를 진행해가는 과정으로 구성된다. 다단계 목표계획법에서는 구조물의 층간 상대변위와 제어시스템의 용량에 대한 설계목표를 여러 단계로 선정하고, 각 물리량과 설계목표간의 정규화된 거리 합으로서 목적함수를 정의한다. 목표갱신 유전자알고리즘은 각 단계별 설계목표를 만족하는 최적해를 검색하고, 현 단계의 모든 설계목표를 만족하는 최적해가 존재할 경우 설계목표를 순차적으로 갱신함으로써 보다 상위수준의 설계목표로 접근해 나아간다. 지진하중을 받는 9층의 빌딩구조물에 대한 수치 예를 통하여 복합구조제어시스템의 통합최적설계 과정을 기술하였고, 구조부재, 수동 및 능동제어시스템이 균등분포된 구조물과 최적 설계결과를 비교하여 제시하는 방법의 효율성을 검증하였다.

• Advances in materials Research
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• 제13권1호
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• 한국항공우주학회지
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• 제42권2호
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• pp.108-118
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• 2014

근사모델을 이용한 최적설계 문제에서는 설계변수의 수가 증가함에 따라 근사모델의 정확도를 확보하기 위한 계산 횟수가 급격히 증가한다. 이를 해결하기 위해 저정확도 모델을 바탕으로 고정확도 모델로 보정하는 Variable-Fidelity Modeling을 이용하였다. 본 논문에서 Variable-Fidelity Model로는 계층적 크리깅 모델을 이용하였으며, 다목적 유전자 알고리즘과 결합하여 최적화 프레임워크를 제안하였다. 이 방법의 유용성을 검증하기 위하여 천음속 영역에 대한 익형 최적 설계를 하였다. 설계변수로는 PARSEC의 파라메터를 이용하였으며, 서로 다른 격자수를 가지는 경우 그리고 서로 다른 정확도를 가지는 해석자를 이용한 경우에 관하여 해석을 수행하였다. 검증을 위해 단일 정확도 모델에 대한 최적화 결과와 비교하였다. 모든 경우에 관하여 파레토 라인이 유사하게 나오는 것을 확인 할 수 있었으며, 계산시간은 계층적 크리깅 모델을 이용한 Variable-Fidelity Model이 단일 정확도 모델에 비하여 훨씬 줄어들었다. 이를 바탕으로 본 논문의 방법이 단일 정확도를 가지는 모델에 대한 최적화 방법과 유사한 정확도를 가지며 더욱 효율적임을 확인 할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4228-4247
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• 2018

Many methods have been developed for the vehicles to create clusters in vehicular ad hoc networks (VANETs). Usually, nodes are vehicles in the VANETs, and they are dynamic in nature. Clusters of vehicles are made for making the communication between the network nodes. Cluster Heads (CHs) are selected in each cluster for managing the whole cluster. This CH maintains the communication in the same cluster and with outside the other cluster. The lifetime of the cluster should be longer for increasing the performance of the network. Meanwhile, lesser the CH's in the network also lead to efficient communication in the VANETs. In this paper, a novel algorithm for clustering which is based on the social behavior of Gray Wolf Optimization (GWO) for VANET named as Intelligent Clustering using Gray Wolf Optimization (ICGWO) is proposed. This clustering based algorithm provides the optimized solution for smooth and robust communication in the VANETs. The key parameters of proposed algorithm are grid size, load balance factor (LBF), the speed of the nodes, directions and transmission range. The ICGWO is compared with the well-known meta-heuristics, Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) for clustering in VANETs. Experiments are performed by varying the key parameters of the ICGWO, for measuring the effectiveness of the proposed algorithm. These parameters include grid sizes, transmission ranges, and a number of nodes. The effectiveness of the proposed algorithm is evaluated in terms of optimization of number of cluster with respect to transmission range, grid size and number of nodes. ICGWO selects the 10% of the nodes as CHs where as CLPSO and MOPSO selects the 13% and 14% respectively.