• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.431-437
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• 2003

In this paper, an approximate optimization program based on GUI(graphic user interface) environment is developed. This program is coded by using Fortran and Visual basic. Fortran is used to Progress approximate optimization process. Visual basic is used to make user environment for user to use conveniently. Inside of this program, it uses two independent programs. One is commercial program, ANSYS, and the other is optimization program, PLBA(Pshenichny-Lim-Belegundu Arora). The former is used to obtain approximate equation of stress and displacement of a structure. The latter is used to solve approximate optimization. This algorithm uses second-order information of a function and active set strategy. This program is connecting ANSYS and PLBA. And it progress the process repeatedly until it obtain optimum value. As a method of approximate optimization, sequential design domain(SDD) is introduced. SDD starts with a certain range which is offseted from midpoint of an initial design domain and then SDD of the next step is determined by optimal point of a prior step.

• International Journal of Ocean System Engineering
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• v.3 no.1
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• pp.1-9
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• 2013

A design procedure for a ship with minimum resistance had been developed using a numerical optimization method called SQP (Sequential Quadratic Programming) combined with computational fluid dynamics (CFD) technique. The frictional resistance coefficient was estimated by the ITTC 1957 model-ship correlation line formula and the wave-making resistance coefficient was evaluated by the potential-flow panel method with the nonlinear free surface boundary conditions. The geometry of the hull surface was represented and modified by B-spline surface modeling technique during the optimization process. The Series 60 ($C_B$=0.60) hull was selected as a parent hull to obtain an optimized hull that produces minimum resistance. The models of the parent and optimized hull forms were tested at calm water condition in order to demonstrate the validity of the proposed methodolgy.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1022-1027
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• 2004

In this study, the shape of plate-fin type heat sink is numerically optimized to acquire the minimum pressure drop under the required temperature rise. To do this, a new sequential approximate optimization (SAO) is proposed and it is integrated with the computational fluid dynamics (CFD). In thermal/fluid systems for constrained nonlinear optimization problems, three fundamental difficulties such as high cost for function evaluations (i.e., pressure drop and thermal resistance), the absence of design sensitivity information, and the occurrence of numerical noise are confronted. To overcome these problems, the progressive quadratic response surface method (PQRSM), which is one of the sequential approximate optimization algorithms, is proposed and the heat sink is optimize by means of the PQRSM.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.12
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• pp.50-55
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• 2012

This paper presents an application of optimization method for efficient operation in micro grid. For operational efficiency, the objective function in a diesel generator consists of the fuel cost function similar to the cost functions used for the conventional fossil-fuel generating plants. The wind turbine generator is modeled by the characteristics of variable output. The cost function of fuel cell plant considers the efficiency of fuel cell. Particle swarm optimization(PSO) and sequential quadratic programming(SQP) are used for solving the problem of microgrid system operation. Also, from the results this paper presents the way to attend power markets which can buy and sell power from upper lever grids by connecting a various generation resources to micro grid.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1198-1203
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• 2008

An efficient sequential optimization approach for metamodel was presented by Choi et al [6]. This paper describes a new approach of the multilevel optimization method studied in Refs. [5] and [21-25]. The basic idea is concerned with multilevel iterative methods which combine a descent scheme with a hierarchy of auxiliary problems in lower dimensional subspaces. After fitting a metamodel based on an initial space filling design, this model is sequentially refined by the expected improvement criterion. The advantages of the method are that it does not require optimum sensitivities, nonlinear equality constraints are not needed, and the method is relatively easy to understand and use. As a check on effectiveness, the proposed method is applied to a classical cantilever beam.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4595-4610
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• 2020

In the practical communication environment, the accurate channel state information (CSI) is difficult to obtain, which will cause the mismatch of resource and degrade the system performance. In this paper, to account for the channel uncertainties, a robust power allocation scheme for a downlink Non-orthogonal multiple access (NOMA) heterogeneous network (HetNet) is designed to maximize energy efficiency (EE), which can ensure the quality of service (QoS) of users. We conduct the robust optimization model based on worse-case method, in which the channel gains belong to certain ellipsoid sets. To solve the non-convex non-liner optimization, we transform the optimization problem via Dinkelbach method and sequential convex programming, and the power allocation of small cell users (SCUs) is achieved by Lagrange dual approach. Finally, we analysis the convergence performance of proposed scheme. The simulation results demonstrate that the proposed algorithm can improve total EE of SCUs, and has a fast convergence performance.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.1
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• pp.31-38
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• 2004

Mechanical design process is generally accomplished by design, analysis, and test. Designers use programs fitting purpose, and obtain repeatedly a response of a simulation program, a sub-program for optimization. In this paper, shape optimization using approximate optimization technique is carried out with sequential design domain(SDD). In addition, algorithm executing Pro/Engineer and ANSYS automatically are adopted in the approximate optimization program by SDD. It is difficult for design problem to be approximated accurately for the whole range of design space. However, more or less accurate approximation is constructed if SDD is applied to that case. SDD starts with a certain range which is off-seted from midpoint of an initial design domain and then SDD of the next step is determined by a move limited. Convergence criterion is defined such that optimal point must be located within SDD during the two steps. Also, the PLBA(Pshenichny-Lim-Belegundu-Arora) algorithm is used to solve approximate optimization problems. This algorithm uses the second-order information and the active set strategy, in order to seek the direction of design variables.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.787-793
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• 2012

A hull form design technique to enhance the wave-making resistance performance for a medium size high speed Ro-Pax ship was studied introducing an optimization method and an automatic hull form modification method. SQP(sequential quadratic programming) was applied as the optimization algorithm and the geometry of hull surface was represented and modified using the NURBS(Non-Uniform Rational B-Spline). The wave-making resistance performance as an objective function in the optimization procedure was evaluated using the Rankine source panel method in which nonlinearity of the free surface boundary conditions and the trim and sinkage of the ship was fully taken into account. Using the Ro-Pax ship as a base hull, the hull-form optimization method was applied to obtain the hull shape that produced the lower wave-making resistance. To verify the validity of the hull-form optimization method, the numerical results was compared with the model test results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.3
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• pp.258-266
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• 2008

The conventional approximate optimization method, which uses the statistical design of experiments(DOE) and response surface method(RSM), can derive an approximated optimum results through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The purpose of this study is to propose a new technique, which is called a sequential design of experiments(SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network(ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently. The suggested algorithm has been applied to various mathematical examples and a structural problem.