• Journal of Energy Engineering
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• v.12 no.4
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• pp.274-280
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• 2003

This paper presents a Case-Sort method to solve the unit commitment problem using database in electric power systems. The formulation of the unit commitment nay be described as nonlinear mixed integer programming. However, it is hard to optimize a problem with discrete and continuous variables in a large-scale system at the same time. The Case-Sort method is based on the unit［MW］generation cost considered drive hour. Then, this paper shows effectiveness and economical efficiency of the proposed algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.112-121
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• 2009

This paper deals with supersonic air-breathing missile system. A supersonic air-breathing missile system has very complicated and incoherent thrust characteristics with respect to outer and inner environment during operation. For this reason, the missile system has many maneuver constraints and is allowed to operate within narrow flight envelope. In this paper, trajectory optimization of the missile is accomplished. The trajectory optimization problem is formulated as a discrete parameter optimization problem. For this formulation, Legendre Pseudo-Spectral method is introduced. This method is based on calculating the state and control variables on Legendre-Gauss-Lobatto (LGL) points. This approach helps to find approximated derivative and integration quantities simply. It is shown that, for this trajectory optimization, trend analysis is performed from thrust characteristics on various conditions so that the trajectory optimization is accomplished with fine initial guess with these results.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.97-97
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• 1988

Genetic Algorithms have been successfully applied to various problems (for example, engineering design problems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with traditional computational techniques. But, several problems for which conventional Genetic Algorithms are ill defined are premature convergence of solution and application of exterior penalty function. Therefore, we developed an Improved Genetic Algorithms (IGAs) to solve above two problems. As a case study, IGAs is applied to several nonlinear optimization problems and it is proved that this algorithm is very useful and efficient in comparison with traditional methods and conventional Genetic Algorithm.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.70-76
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• 1999

In this paper, a mixed-integer programming approach is presented for adjusting the voltage profiles in a power system. The advent of large-scaled system makes the reactive power and voltage problem-an attempt to achieve an overall improvement of system security, service quality and economy-more complex and seriously, Although the problem is originally a nonlinear optimization problem, it can be formulated as a mixed integer linear programming(MILP) problem without deteriorating of solution accuracy to a certain extent. The MILP code is developed by the branch and bound process search for the optimal solution. The variable for modeling transformer tap positions is handled as discrete one, and other variables continuous ones. Numerical data resulting from case study using a modified IEEE 30 bus system with outaged line show that the MILP can produce more reductions of magnitude in the operating cost. The convergence characteristics of the results are also presented and discussed.

• Journal of Ocean Engineering and Technology
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• v.23 no.6
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• pp.131-135
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• 2009

We present an optimum design method for a rectangular reinforced concrete beam using Genetic Algorithms. The optimum design procedure in this paper employs 2 design cases: i) all of the design variables (b, d, As) of the rectangular reinforced concrete section are used pseudo-continuously, ii) one is pseudo-continuous for the concrete cross section (b, d) and the other is discrete, using an index for the steel area (As). The optimum design in this paper uses Chakrabarty's model. In this paper, the Genetic Algorithms use the method of Elitism and penalty parameters to improve the fitness in the reproduction process, which leads to very practical designs. The optimum design of the steel area in the examples uses ASTM standard reinforcing bars (#3~#11, #14, #18).

• 한국전산유체공학회:학술대회논문집
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• 2000.05a
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• pp.66-72
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• 2000

Aerodynamic sensitivity analysis is performed for the Navier-Stokes equations coupled with two-equation turbulence models using a discrete adjoint method and a direct differentiation method respectively. Like the mean flow equations, the turbulence model equations are also hand-differentiated to accurately calculate the sensitivity derivatives of flow quantities with respect to design variables in turbulent viscous flows. Both the direct differentiation code and the adjoint variable code adopt the same time integration scheme with the flow solver to efficiently solve the differentiated equations. The sensitivity codes are then compared with the flow solver in terms of solution accuracy, computing time and computer memory requirements. The sensitivity derivatives obtained from the sensitivity codes with different turbulence models are compared with each other. Using two-equation turbulence models, it is observed that a usual assumption of constant turbulent eddy viscosity in adjoint methods may lead to seriously inaccurate results in highly turbulent flows.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.175-183
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• 1998

When a product is designed, tolerances must be given to the product so that required functions are guaranteed and production costs are minimized. In this research, a model is suggested which allocates tolerances to components optimally according to the STA(Statistical Tolerance Allocation) method. Taking into account the concept that dimensional errors have characteristics of statistical distributions, this model presents the discrete pseudo-boolean approach for the tolerance optimization by minimizing the tolerance cost and the quality loss cost. In this approach, two methods are proposed for the reduction of the problem scale; 1) a method for converting the minimization model for casts into the maximization model for cost savings, and 2) procedures to reduce the number of constraints and variables.

• Steel and Composite Structures
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• v.24 no.1
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• pp.129-140
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• 2017

Structural optimization is one of the popular and active research areas in the field of structural engineering. In the present study, the newly developed moth-flame optimization (MFO) algorithm and its enhanced version termed as enhanced moth-flame optimization (EMFO) are employed to implement the optimization process of planar and 3D steel frame structures with discrete design variables. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. A number of benchmark steel frame optimization problems are solved by the MFO and EMFO algorithms and the results are compared with those of other meta-heuristics. The obtained numerical results indicate that the proposed EMFO algorithm possesses better computational performance compared with other existing meta-heuristics.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4E
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• pp.53-57
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• 1996

An extension of the well-known Fourier transform is developed in this paper. It is denoted as the generalized Fourier transform(GFT), since it encompasses the Fourier transform as its special case. The first idea of this extension can be found on [1]. In the definition of the N-point discrete GFT, it first construct a passband in time which functions as a window in the time domain. An appropriate interpretation of each variables are introduced during the definition of the GFT, followed by the formal derivation of the inverse GFT. This transform pair is similar to the windowing in the frequency domain such as the subband coding technique (or filter bank approach) and could be extended to the wavelet transform.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.171-178
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• 2004

The purpose of optimum design for structures is to minimize the cost and to obtain the reasonable structural systems. This design algorithm have many objective functions including discrete variables as sections, weight, stiffness and shapes. Simulated annealing, Genetic algorithm and TABU algorithm are used search for these optimum values in the structural design. TABU algorithm is applied to many types structures to search for section and distribution optimization and compared with the results of Genetic algorithm for evaluating the efficiency of this algorithm. In this paper, the plane truss of 10 elements and the space truss of 25 element having 10 nodes, star dome and cable dome are analyzed as analytical models.