• Advances in Computational Design
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• 제1권3호
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• 한국강구조학회 논문집
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• 제18권2호
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• pp.147-160
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• 2006

본 논문에서는 개선소성힌지해석과 유전자 알고리듬을 이용한 평면 강골조 구조물의 퍼지최적설계 방법을 제시하였다. 개선소성힌지해석에서는 강골조 구조물의 기하학적 비선형성을 고려하기 위해 보-기둥 요소의 안정함수를 사용하였으며, 재료적 비선형을 고려하기 위해 잔류응력, 소성힌지, 그리고 기하학적 불완전성 등에 의한 점진적인 강성감소모델을 사용하였다. 유전자 알고리듬에서는 토너먼트 선택방법과 마이크로 유전자 알고리즘을 사용하였다. 목적함수로는 구조물의 총중량을 사용하였으며, 제약조건으로는 하중-저항능력, 사용성, 연성도, 그리고 시공성에 관한 기준을 고려하였다. 퍼지최적설계에서는 명확한 목적함수와 퍼지제약을 가지는 경우에 한하여 허용 오차는 제한값의 5%로 선택하고 비소속함수와 레벨컷 방법을 이용하여 0에서 1까지 0.2간격으로 나누어 최적화하였다. 여러 평면 강골조 구조물의 최적설계를 수행하여 일반GA최적설계와 퍼지GA최적설계의 최적값을 비교하였다.

• 산업경영시스템학회지
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• 제40권1호
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Steel and Composite Structures
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• 제15권5호
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• pp.519-538
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• 2013

The design optimization of a cold-formed steel portal frame building is considered in this paper. The proposed genetic algorithm (GA) optimizer considers both topology (i.e., frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables. Previous GAs in the literature were characterized by poor convergence, including slow progress, that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is shown to be an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Thus, population diversity is maintained and premature convergence is reduced significantly. Through benchmark examples, it is shown that the efficient GA proposed generates optimal solutions more consistently. A parametric study was carried out, and the results included. They show significant variation in the optimal topology in terms of pitch and frame spacing for a range of typical column heights. They also show that the optimized design achieved large savings based on the cost of the main structural elements; the inclusion of knee braces at the eaves yield further savings in cost, that are significant.

• 대한기계학회논문집A
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• 제29권10호
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• pp.1369-1376
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• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.606-609
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• 2015

본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

• Journal of Electrochemical Science and Technology
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• 제14권2호
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• pp.174-183
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• 2023

The dye wastewater discharge from the textile industries mainly affects the aquatic environment. Hence, the treatment of this wastewater is essential for a pollutant-free environment. The purpose of this research is to optimize the dye removal efficiency for process influencing independent variables such as pH, electrolysis time (ET), and current density (CD) by using Box-Behnken design (BBD) optimization and Genetic Algorithm (GA) modelling. The electrocoagulation treatment technique was used to treat the synthetically prepared Reactive Black dye solution under batch mode potentiometric operations. The percentage of error for the BBD optimization was significantly greater than for the GA modelling results. The optimum factors determined by GA modelling were CD-59.11 mA/cm², ET-24.17 minutes, and pH-8.4. At this moment, the experimental and predicted dye removal efficiencies were found to be 96.25% and 98.26%, respectively. The most and least predominant factors found by the beta coefficient were ET and pH respectively. The outcome of this research shows GA modeling is a better tool for predicting dye removal efficiencies as well as process influencing factors.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권3호
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• 한국공간구조학회:학술대회논문집
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• 한국공간구조학회 2008년도 춘계 학술발표회 논문집
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• pp.101-106
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• 2008

본 연구에서는 서로 상이한 최적화기법인 유전알고리듬과 수학적 프로그래밍기법을 이용하여 지오데식 돔 구조물을 최적화하고 그 결과를 분석하였다. 본 연구에서 도입한 유전알고리즘은 유전연산자인 선택, 교배, 돌연변이 이외에도 재생기법을 도입하여 최적해의 검색성능을 높였다. 그리고 수학적인 프로그래밍기법은 유한차분법을 이용하여 목적함수의 설계민감도를 계산하였으며 세 가지의 다른 검색기법을 이용하여 돔의 크기최적화를 수행하였다. 지오데식 돔의 중앙에 작용하는 집중하중에 저항하는 돔의 각 부재의 크기패턴을 자체 개발된 $ISADO-GA{\alpha}$와 ISADO-OPT를 이용하여 최적 설계하였다. 본 연구를 통하여 제시된 최적결과는 정해가 존재하지 않는 실제 구조물의 최적 값에 대한 유용한 정보를 제공할 뿐만이 아니라 향후 대공간구조의 새로운 구조시스템 개발의 밑거름이 될 것으로 판단된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.263-268
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• 2010

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of in dividuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become to real offspring. From this multiple offspring competition, our GA rarel falls into the premature convergence and easily gets out of the local optimum areas without negative effects. This makes our GA fast evolve to the global optimum. Experimental results with four function optimization problems showed that our method was superior to the original GA and had similar performances to the best ones of queen-bee GA with best parameters.