• Management Science and Financial Engineering
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• 제12권1호
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• pp.113-125
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• 2006

Bilevel programming problem is a two-stage optimization problem where the constraint region of the first level problem is implicitly determined by another optimization problem. In this paper we consider the bilevel quadratic/linear fractional programming problem in which the objective function of the first level is quasiconcave, the objective function of the second level is linear fractional and the feasible region is a convex polyhedron. Considering the relationship between feasible solutions to the problem and bases of the coefficient submatrix associated to variables of the second level, an enumerative algorithm is proposed which finds a global optimum to the problem.

• Smart Structures and Systems
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• 제12권6호
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• pp.641-659
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• 2013

In this paper is studied the influence of the uncertain mass distribution over the floors on the choice of the optimal parameters of a hybrid control system for tall buildings subjected to wind load. In particular, an optimization procedure is developed for the robust design of a hybrid control system that is based on an enhanced Monte Carlo simulation technique and the genetic algorithm. The large computational effort inherent in the use of a MC-based procedure is reduced by the employment of the Latin Hypercube Sampling. With reference to a tall building modeled as a multi degrees of freedom system, several numerical analyses are carried out varying the parameters influencing the floors' masses, like the coefficient of variation of the distribution and the correlation between the floors' masses. The procedure allows to obtain optimal designs of the control system that are robust with respect to the uncertainties on the distribution of the dead and live loads.

• 한국전산유체공학회지
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• 제4권2호
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• pp.67-73
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• 1999

In the present paper, three types of the flow solvers were used to investigate the influence on the airfoil shape optimization. The adopted equations, i.e., Euler, thin layer Navier-Stokes and full Navier-Stokes ones. are solved using implicit LU-ADI decomposition scheme. The gradient projection method with the sinusoidal function was used as an optimization algorithm. The present numerical method was applied to the drag minimization problems under the initial shape of NACA0012 airfoils.

• 한국수자원학회논문집
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• 제51권9호
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• pp.803-812
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• 2018

본 연구는 다목적함수를 고려한 입자군집최적화(Particle Swarm Optimization, PSO) 알고리즘을 Python으로 개발하고, Soil and Water Assessment Tool (SWAT) 모형에 적용하여 자동보정 알고리즘의 적용 가능성을 평가하였다. SWAT 모형의 유출 해석은 안성천의 공도 수위 관측소 상류유역($364.8km^2$)을 대상으로 하였으며, 공도 지점의 2000년부터 2015년까지의 일 유량 자료를 이용하였다. PSO 자동보정은 결정계수(coefficient of determination, $R^2$), 평균제곱근오차(RMSE), NSE 모형효율계수(Nash-Sutcliffe Efficiency, $NSE_Q$), 특히 중간유출과 기저유출의 보정을 위해 $NSE_{INQ}$ (Inverse Q)를 활용하여 SWAT을 보정하였다. PSO을 통한 SWAT 모형의 자동보정과 수동보정의 유출해석 결과, 각각 $R^2$는 0.64, 0.55, RMSE는 0.59, 0.58, $NSE_Q$는 0.78, 0.75, $NSE_{INQ}$는 0.45, 0.09의 상관성 분석결과를 보였다. PSO 자동보정 알고리즘은 수동보정에 비하여 높은 향상을 보였는데 특히 유출의 감수곡선을 개선시켰으며 적절한 매개변수 추가(RCHRG_DP)와 매개변수 범위의 설정으로 수동보정의 한계를 보완하였다.

• Geomechanics and Engineering
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• 제31권4호
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• 한국지반환경공학회 논문집
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• 제7권1호
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• pp.41-53
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• 2006

본 연구에서는 아연 및 카드뮴을 대상으로 수행된 흡착실험과 칼럼확산실험 결과를 바탕으로 유전자 알고리즘을 이용한 최적화 과정을 통하여 비선형 흡착 모델 및 이류-확산 모델식의 파라미터들을 추정하여 보았다. 수행결과 비선형 흡착 식 (Langmuir 흡착모델과 Freundlich 흡착모델) 들의 모델파라미터 추정은 이들 흡착식 들의 선형화 과정을 거쳐 얻어진 파라미터들과 거의 일치하는 결과를 얻을 수 있었다. 오염물질의 이동 해석을 위해 수행된 이류-확산 모델의 유한요소해석과 모델 파라미터 추정을 위해 수행된 최적화 과정을 통해 얻은 아연과 카드뮴의 확산계수는 선형 분배계수를 이용할 경우 두 금속 모두에서 약 $10^{-7}cm^2/s$ 차원의 확산계수를 얻을 수 있었다. 또한 비선형 흡착 모델로부터 얻어진 지연인자를 이용할 경우 두 금속 모두에서 $10^{-6}{\sim}10^{-5}cm^2/s$ 범위의 확산계수 값을 얻을 수 있었다. 결론적으로 유전자 알고리즘을 이용한 최적화 과정을 통한 비선형 흡착식 및 이류-확산 모델의 파라미터 추정은 성공적으로 수행될 수 있었고, 실측값과 최적화 과정을 거쳐 예측된 값 사이의 상관계수는 0.9 이상으로 높은 상관성을 보이는 것으로 나타났다.

• Earthquakes and Structures
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• 제5권1호
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• pp.83-109
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• 2013

A simple damper optimization method is proposed to find optimal damper allocation for shear buildings under both target added damping ratio and interstorey drift ratio (IDR). The damping coefficients of added dampers are considered as design variables. The cost, which is defined as the sum of damping coefficient of added dampers, is minimized under a target added damping ratio and the upper and the lower constraint of the design variables. In the first stage of proposed algorithm, Simulated Annealing, Nelder Mead and Differential Evolution numerical algorithms are used to solve the proposed optimization problem. The candidate optimal design obtained in the first stage is tested in terms of the IDRs using linear time history analyses for a design earthquake in the second stage. If all IDRs are below the allowable level, iteration of the algorithm is stopped; otherwise, the iteration continues increasing the target damping ratio. By this way, a structural response IDR is also taken into consideration using a snap-back test. In this study, the effects of the selection of upper limit for added dampers, the storey mass distribution and the storey stiffness distribution are all investigated in terms of damper distributions, cost function, added damping ratio and IDRs for 6-storey shear building models. The results of the proposed method are compared with two existing methods in the literature. Optimal designs are also compared with uniform designs according to both IDRs and added damping ratios. The numerical results show that the proposed damper optimization method is easy to apply and is efficient to find optimal damper distribution for a target damping ratio and allowable IDR value.

• Computers and Concrete
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• 제32권6호
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• 대한전자공학회논문지SP
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• 제44권3호
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• pp.23-32
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• 2007

H.264 영상 부호화 표준은 인트라 예측에서 압축 효율을 향상시키기 위해 율-왜곡 최적화(RDO : Rate Distortion Optimization) 방법을 사용한다. 이러한 방법을 사용함으로써 현재 블록에 대한 최적의 부호화 모드의 선택이 가능해졌지만 복잡도와 연산은 이전대비 더욱 증가하였다. 본 논문은 우세한 에지 방향(DED : Dominant Edge Direction)의 예측을 통한 고속인트라 모드 결정 알고리즘을 제안한다. 이를 위해 이 알고리즘은 이산 코사인 변환(DCT : Discrete Cosine Transform) 계수를 근사화하여 이용한다. DED를 예측함으로써 $4{\times}4$ 휘도 블록의 경우 최적 모드 결정을 위한 율-왜곡 최적화 계산에 9개 모드 중 3개 모드가 선택된다. $16{\times}16$ 휘도 블록과 $8{\times}8$ 색상 블록의 경우 4개 모드 대신에 2개 모드가 최적 모드 결정을 위해 율-왜곡 최적화 계산을 수행한다. 이러한 방법을 이용한 실험 결과 인트라 전체 검색 방법대비 약 72%의 연산시간이 감소하는 결과를 보여준다.

• 전기학회논문지
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• 제58권4호
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.