• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.69-80
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• 1999

A genetic algorithm to determine the optimal signal sequence and double cycle pattern is described. The signal sequence and double cycle pattern are used as the input for TRANSYT to find optimal signal timing at each junction in the area traffic networks, In the genetic process, the partially matched crossover and simple crossover operators are used for evolution of signal sequence and double cycle pattern respectively. A special conversion algorithm is devised to convert the signal sequence into the link-stage assignment for TRANSYT. Results from tests using data from an area traffic network in Leicester region R are given.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.69-73
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• 1978

This paper deals with the problem of determining the optimal inventory-transportation policy of the idealistically simple inventory-transportation system with the following assumptions: (1) The system consists of a single central warehouse and a single local warehouse, (2) The planning horizon is finite, (3) Demand rate is fixed costant, and so forth. Developed is the algorithm by which to identify the optimal inventory policy which minimizes the total cost incurred to the system over the given finite planning horizon. A sample numerical example is presented along with a discussion of the possible applications of the approach used n the algorithm.

• Korean Management Science Review
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• v.17 no.2
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• pp.1-12
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• 2000

In this study, we deals with the implementation of an optimal basis identification procedure for interior point methods. Our implementation is based on Megiddo’s strongly polynomial algorithm applied to Andersen and Ye’s approximate LP construction. Several techniques are explained such as the use of effective indicator for obtaining optimal partition when constructing the approximate LP, the efficient implementation of the problem reduction technique proposed by Andersen, the crashing procedure needed for fast dual phase of Megiddo’s algorithm and the construction of the stable initial basis. By experimental comparison, we show that our implementation is superior to the crossover scheme implementation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.156-161
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• 1994

In this paper, we consider an optimal control problem of a nonlinear stochastic system. Dynamic programming approach is employed for the formulation of a stochastic optimal control problem. As an optimality condition, dynamic programming equation so called the Bellman equation is obtained, which seldom yields an analytical solution, even very difficult to solve numerically. We obtain the numerical solution of the Bellman equation using an algorithm based on the finite difference approximation and the contraction mapping method. Optimal controls are constructed through the solution process of the Bellman equation. We also construct a test case in order to investigate the actual performance of the algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.346-354
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• 1992

In the power-system, the active and reactive power levels of load bus randomly vary over days, months, and years which are stochastic in nature. This paper presents an algorithm for optimal Var planning considering the uncertainties of loads. The optimization problem is solved by a stochastic linear programming technique which can handle stochastic constraints to evaluate optimal Var requirement at load bus to maintain the voltage profile which results in probabilistic density function by stochastic Load Flow analysis within admissible range. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Journal of applied mathematics & informatics
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• v.33 no.3_4
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• pp.387-399
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• 2015

In this paper, a new smoothing approximation to the l₁ exact penalty function for constrained optimization problems (COP) is presented. It is shown that an optimal solution to the smoothing penalty optimization problem is an approximate optimal solution to the original optimization problem. Based on the smoothing penalty function, an algorithm is presented to solve COP, with its convergence under some conditions proved. Numerical examples illustrate that this algorithm is efficient in solving COP.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1155-1158
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• 1999

In this paper, we consider the optimal control of detection threshold to minimize the conditional mean-square state estimation error for the probabilistic data association (PDA) filter. Earlier works on this problem involved the cumbersome graphical optimization algorithm or time-consuming numerical optimization algorithm. Using the numerical approximation of information reduction factor, we obtained the closed-form optimal detection threshold. This results are very useful for real-time implemenation.

• CDE review
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• v.21 no.2
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• pp.34-38
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• 2015

In case of the big city, choosing the adequate root of which we can reach the destination can affect the driver's condition and driving time. so it is quite important to find the optimal routes for arriving the destination as considering the factors, such as driving conditions or travel time and so on. In this paper, we develop route choice model with considering driving conditions and travel time, and it can search the optimal path which make drivers reduce their fatigues using genetic algorithm.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.224-226
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• 1997

This paper describes a procedure for the optimal design of induction motor for electric vehicle. The immune algorithm is applicated for the optimal solution search. To verify the validity of the proposed method, a example disign for output 20[kw] is performed.

• Proceedings of the KIEE Conference
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• summer
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• pp.932-934
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• 2004

BLDC Motor is used in robots requiring a precisive motion recently. This paper presents the optimal design reducing the rotor inertia in order to improve the driving characteristic of BLDC motor driving robots, The optimal design was performed by using a parallel Genetic Algorithm which is superior at searching objective functions for the comflicated models having several optimal points. Therefore, objective function for optimization is rotor inertia and efficiency.