• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.587-592
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by simplex method in reconstructive search space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• Journal of the military operations research society of Korea
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• v.11 no.1
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• pp.79-86
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• 1985

A multi-interchange simplex method is presented. This method tries to cut almost half of the set of convex combinations which generate all decreasing feasible directions. Analysis of this method indicates high possibility of the existence of polynomial-time simplex-type methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.4
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• pp.209-215
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• 1993

A stability analysis program(CSLOP) which can obtain the factor of safety and the critical failure surface at the same time is developed from this study. In order to improve existing slice methods, the general slice method of Gussmann is applied to compute the factor of safety, and the constrained simplex algorithm proposed by Box is applied to systematically locate the critical failure surface in slopes. The proposed method is applied to two examples of circular and noncircular slope stability and the results are compared to previously published solutions. Constrained simplex method is recommended to improve slope stability programs which provide for an automatic search of the critical failure mechanisms.

• Journal of the military operations research society of Korea
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• v.9 no.2
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• pp.57-60
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• 1983

We show that the complexity of Simplex Method for Linear Programming problem is equivalent to the complexity of finding just an adjacent basic feasible solution if exists. Therefore a simplex type method which resolves degeneracy in polynomial time with respect to the size of the given linear programming problem can solve the general linear programming problem in polynomial steps.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.3 no.3
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• pp.49-53
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• 1980

Computer program about the simplex method -one of the most popularity used linear programming- has been studied. Variable and subject to the restriction are growing according to the growing of all of the problem. In this paper, the problem that can be solved by simplex method may be processed with enormous block unit ( $50\;{\times};\50$ ). Because of using computer all of the problem can be solved more accurately and quickly. In appendix, computer program and running example are recorded.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.52-55
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• 1997

In this paper, both the generalization of one artificial variable technique to the general bound problem and the efficient implementation of the technique are suggested. When the steepest-edge method is used as a pricing rule in the simplex method, it is easy to update the reduced cost and the simplex multiplier every iteration. Therefore, one artificial variable technique is more efficient than Wolfe's method in which the reduced cost and simplex multiplier must be recalculated in every iteration. When implementing the one artificial variable technique on the LP problems with the general bound restraints on the variables, an arbitrary basic solution which satisfies the bound restraints is sought first, and the artificial column which adjusts the infeasibility is introduced. The phase one of the simplex method minimizes the one artificial variable. The efficient implementation technique includes the splitting, scaling, storage of the artificial column, and the cure of infeasibility problem.

• Applied Biological Chemistry
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• v.51 no.4
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• pp.338-341
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• 2008

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.797-802
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• 2005

We generate simplex polynomials by using a method, which produces an OPS in (d + 1) variables from an OPS in d variables and the Jacobi polynomials. Also we obtain a partial differential equation of the form $${\Sigma}_{i,j=1}^{d+1}\;A_ij{\frac{{\partial}^2u}{{\partial}x_i{\partial}x_j}}+{\Sigma}_{i=1}^{d+1}\;B_iu\;=\;{\lambda}u$$, which has simplex polynomials as solutions, where ${\lambda}$ is the eigenvalue parameter.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.24-29
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• 1996

If the LP problem doesn't have the optimal soultion uniquely, the solution fo the primal-dual barrier method converges to the interior point of the optimal face. Therefore, when the optimal vertex solution or the optimal basis is required, we have to perform the additional procedure to recover the optimal basis from the final solution of the interior point method. In this paper the exisiting methods for recovering the optimal basis or identifying the optimal solutions are analyzed and the new methods are suggested. This paper treats the two optimal basis recovery methods. One uses the purification scheme and the simplex method, the other uses the optimal face solutions. In the method using the purification procedure and the simplex method, the basic feasible solution is obtained from the given interior solution and then simplex method is performed for recovering the optimal basis. In the method using the optimal face solutions, the optimal basis in the primal-dual barrier method is constructed by intergrating the optimal solution identification technique and the optimal basis extracting method from the primal-optimal soltion and the dual-optimal solution.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.99-101
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• 2000

Different optimization algorithms have been proposed to solve real and reactive power optimization problems. Most of all, linear programming techniques that employed a simplex method have been extensively used. But, the growth in the size of power systems demands faster and more reliable optimization techniques. An Interior Point(IP) mehod is based on an interior point approach to aim the solution trajectory toward the optimal point and is converged to the solution faster than the simplex method. This paper deals with the use of Successive Linear Programming(SLP) for the solution of the Security Constrained Economic Dispatch(SCED) problem. This problem is solved using the IP method. A comparison with simplex method shows that the interior point technique is reliable and faster than the simplex algorithm.