• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.292-297
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• 2004

본 연구에서는 단일목적 저수지와 다목적 댐의 최적운영을 위하여 전역최적해를 탐색하는 SCE-UA법을 사용하는 비선형계획법을 적용한 최적화 모형을 구성하고 과거 운영자료를 사용하여 모형의 적용성을 검토하고 분석하였다. 또한, 다목적댐의 운영수위 상승으로 인하여 발생하는 추가용수를 댐하류로 추가적으로 공급함에 따른 댐운영상의 문제점과 해결책을 제시했다. 관개용 단일 목적 저수지의 유입량은 하천망 모형인 SSARR 모형을 이용하여 추정하였다. 관개용 단일 목적저수지의 용수배분을 최적화한 결과, 실측치와 최적방류량간의 상대오차가 $-2.6\~10.5\%$ 범위를 나타냈으며, 비교적 실측방류량과 유사한 형태로 용수를 공급하는 길과를 나타냈다. 다목적 저수지의 최적운영을 위해 발전량, 저수량 및 필요수량의 관계를 목적함수로하는 최적화 모형을 구성하여 섬진강댐의 최적운영에 적용하였다. 섬진강댐의 댐하류 방류량 증가에 따른 운영상의 문제점을 해결하기 위하여 댐하류 유지용수량을 0.17, 0.50, 0.70, 1.0, 1.5, $3.0m^3/sec$ 방류하는 경우로 구분하여 최적운영한 길과, 댐하류 유지용수량이 $1.0m^3/sec$ 이하인 경우에 발전량이 실적평균발전량에 근접한 결과를 나타냈으며, 용수공급량도 계획공급량인 377.4 백만 $m^3$ 보다 $28.9\~100.7$ 백만 $m^3$ 만큼 많은 양을 공급하는 결과를 나타냈다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.987-992
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• 2002

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.11-17
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• 2012

The VLC system undertakes both missions of illumination and wireless communication. It is difficult to design a VLC system with optimal performance due to the trade-offs between power consumption and channel quality. In this paper, the VLC system design problem is solved by using multi-objective optimization method. For optimization, the multi-objective function is formulated with respect to power consumption, received power, and SNR under the constraints on the system variables. Through the multi-objective optimization, it is possible to obtain the solutions that satisfies both minimum power consumption and maximum channel quality.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.12
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• pp.1793-1803
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• 2010

The weighting method and goal programming require weighting factors or target values to obtain a Pareto optimal solution. However, it is difficult to define these parameters, and a Pareto solution is not guaranteed when the choice of the parameters is incorrect. Recently, the Mahalanobis Taguchi System (MTS) has been introduced to minimize the Mahalanobis distance (MD). However, the MTS method cannot obtain a Pareto optimal solution. We propose a function called the skewed Mahalanobis distance (SMD) to obtain a Pareto optimal solution while retaining the advantages of the MD. The SMD is a new distance scale that multiplies the skewed value of a design point by the MD. The weighting factors are automatically reflected when the SMD is calculated. The SMD always gives a unique Pareto optimal solution. To verify the efficiency of the SMD, we present two numerical examples and show that the SMD can obtain a unique Pareto optimal solution without any additional information.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.1
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• pp.26-35
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• 1987

As an attempt to make the multi-objection for the line design of the rope way, the resulted formulas from the catenary curve as exact ones were summarized, and it was found out that the Kuhn-Tucker's optimality conditions and regions of the objective functions can analytically be expressed with dimensionless parameters. The Pareto's optimum solution set was analytically obtained through the objective function-the minimum relation of $W^{*}$, and $W^{*}$ is a trade-off relation. From this, The dimension of a rope and the value of an initial tension that are the standard in design of the rope way were determined. It was concluded that $V^{*}$ should become minimum, and that the ratio of the dimension of rope to the value of and initial tension become larger than superposition factor corresponding to curve AB.to curve AB.

• Journal of Korea Water Resources Association
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• v.45 no.1
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• pp.15-27
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• 2012

SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.124-127
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• 2017

선박에서 배출되는 환경오염 물질 및 온실가스에 대한 규제가 강화됨에 따라, 환경오염 물질 및 온실가스의 배출과 직접적으로 관련있는 연료 소모량을 줄이려는 다양한 연구가 진행되고 있다. 연료 소모량을 줄이기 위한 방안 중 하나는 환경 및 기상 예보를 이용하여 연료가 가장 적게 소모되는 항로를 찾는 것이다. 기존 연구에서는 연료 소모량을 주된 목적함수로 최소화 하되, 도착 시간에 대한 조건을 평가하기 위해 도착 시간의 기댓값을 계산하고 추가적인 목적함수로 고려하는 경우가 많았다. 그러나 선박 운항 예측 시 적용되는 환경 외란 정보는 상당한 불확실성을 포함하고, 이로 인해 발생하는 운항 속도 및 도착 시간에 대한 불확실성도 상당히 클 수 있기 때문에, 도착 시간의 기댓값뿐만 아니라 도착 시간에 대한 불확실성을 기반으로 제한 시간 내에 선박이 도착할 확률을 정량적으로 평가하는 것이 필요하다. 본 연구에서는 다목적 최적화 기법을 이용해 도착 시간의 기댓값과 연료 소모량에 대한 Pareto set을 구하되, 환경 외란으로부터 발생하는 도착 시간의 불확실성을 계산하여, 제한 시간 내에 선박이 도착할 확률을 계산하고 이를 항로 최적화 시 적용한다. 제안하는 방법의 유용성을 검증하기 위해 실제 환경에 가까운 맵을 기반으로 부산-도쿄 간의 항로를 최적화하고, 그 결과에 대해 논의한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.433-441
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• 2011

The optimization of a centrifugal compressor was conducted. The ANN (Artificial Neural Network) was adopted as an optimization algorithm, and it was learned and trained with the DOE (Design of Experiment). In the DOE, it was predicted the main effect and the interaction effect of design variables to the objective function. The ANN was improved in the optimization process using the GA (Genetic Algorithm). When any output at each generation was reached a standard level, it was re-calculated by the CFD (Computational Fluid Dynamics) and it was applied to develop a new ANN. After 6th generation, the prediction difference between ANN and CFD was less than 1%. A pareto of the efficiency versus the pressure ratio was obtained through the 21th generation. Using this method, the computational time for the optimization was equivalent to the time consumed by the gradient method, and the optimized results of multi-objective function were obtained.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.509-515
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• 2013

In this study, the real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was developed for an airfoil shape design. In order to achieve the better aerodynamic characteristics than reference airfoil at landing and cruise conditions, maximum lift coefficient and lift-to-drag ratio were chosen as object functions. Futhermore, the PARSEC method reflecting geometrical properties of airfoil was adopted to generate airfoil shapes. Finally, two airfoils, which show better aerodynamic characteristics than a reference airfoil, were chosen. As a result, maximum lift coefficient and lift-to-drag ratio were increased of 4.89% and 5.38% for first candidate airfoil and 7.13% and 4.33% for second candidate airfoil.