• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.34-41
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• 1999

This paper presents a HGA-based(hybrid genetic algorithm) optimal control strategy to control of the swing motion and the transfer of the overhead crane. The objective is to achieve the regulation of the fast swing motion or fast position control. The controller is based on the state feedback. The HGA-based optimal algorithm is applied to find optimal gains of the controller. Computer simulation and experiments were performed to demonstrate the effectiveness of the proposed control scheme.

• 산업공학
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• 제22권3호
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• pp.223-233
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• 2009

In this paper we developed a method for an optimal planning to reorganize Dong offices to enhance the administrative efficiency. First we defined a mathematical model for the optimal planning problem of reorganizing Dong office and developed a genetic algorithm to solve the problem. For the purpose of minimizing standard deviation of population, area and distance among reorganized offices, the constraints such as allocation, distance, area, population, etc. are considered and weights are applied to Dong offices in the downtown and shopping area. The developed algorithm was applied for reorganizing Dong offices in Jongro Gu, Seoul. The results showed that the developed algorithm could be applied for the real world problem. This study may be applied to the optimal decision of reorganization of offices in the similar reorganization or company M&A situations by changing constraints and weights.

• 대한전기학회논문지:전력기술부문A
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• 제51권6호
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• pp.302-310
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• 2002

This paper presents an application of a improved branch exchange (IBE) algorithm with a tie branch power (TBP) flow equation to solve the Optimal Routing problem for operation of a radial Power system including power distribution system. The main objective of the Optimal Routing problem usually is to minimize the network real power loss and to improve the voltage profile in the network. The new BE algorithm adopts newly designed methods which are composed by decision method of maximum loss reduction and new index of loss exchange in loop network Thus, the proposed algorithm in this paper can search the optimal topological structures of distribution feeders by changing the open/closed states of the sectionalizing and tie switches. The proposed algorithm has been evaluated with the practical IEEE 32, 69 bus test systems and KEPCO 148 bus test system to show favorable performance gained.

• 품질경영학회지
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• 제36권1호
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• pp.40-48
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• 2008

The purpose of this study is to rationally manage service facilities of convention center. This study is to develop the algorithm to consider optimal assignment and optimal operation system planning for convention center. The scheduling algorithm of this study develops through constructing the mathematical model and analyzing the mathematical structure of variables and constraints in model. The scheduling algorithm develops to consist eight stage of optimal operation planning and five stage of optimal assignment planning. Especially, this study indicates that optimum answer through mathematical model and results of algorithm is nondiscrimination.

• International Journal of Korean Welding Society
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• 제3권2호
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• 한국컴퓨터정보학회논문지
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• 제20권12호
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• pp.75-82
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• 2015

Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm of two-sided optimization with time complexity $O(n^4)$. This paper suggests one-sided optimal assignment and swap optimization algorithm with time complexity $O(n^2)$ can be achieve the goal of two-sided optimization. This algorithm selects the minimum cost for each row, and reassigns over-assigned to under-assigned cell. Next, that verifies the existence of swap optimization candidates, and swap optimizes with ${\kappa}-opt({\kappa}=2,3)$. For 27 experimental data, the swap-optimization performs only 22% of data, and 78% of data can be get the two-sided optimal result through one-sided optimal result. Also, that can be improves on the solution of best known solution for partial problems.

• Journal of Welding and Joining
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• 제18권5호
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• pp.63-69
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• 2000

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables was wire feed rate, welding voltage, and welding speed and the output variables were bead height, bead width, and penetration. The number of level for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 40 experiments.

• 한국산학기술학회논문지
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• 제14권1호
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• pp.7-12
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• 2013

본 논문은 연삭 공정의 최적화 알고리즘과 이를 구현하기 위한 방안을 제안하였다. 최적의 연삭 공정 설계를 위해서 최적화 함수를 제안하고 선정된 최적 함수의 해를 구하기 위해 DE(Differential Evolution)알고리즘을 이용하였다. 알고리즘의 구현은 산업현장에서 널리 사용되고 있는 LabView소프트웨어를 통해 구현하였고 컴퓨터 시뮬레이션을 통해 제안된 알고리즘을 검증하였다. 본 논문에서 획득한 최적화 기법은 연삭공정의 가이드라인으로 활용 될 수 있을 것으로 사료된다.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권4호
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• pp.198-204
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• 2003

In this paper, we presented a new algebraic iterative algorithm for the optimal control of the nonlinear systems. The algorithm is based on two steps. The first step transforms nonlinear optimal control problem into a sequence of linear optimal control problem using the quasilinearization method. In the second step, TPBCP(two point boundary condition problem) is solved by algebraic equations instead of differential equations using the new integral operational matrix of BPF(block pulse functions). The proposed algorithm is simple and efficient in computation for the optimal control of nonlinear systems and is less error value than that by the conventional matrix. In computer simulation, the algorithm was verified through the optimal control design of synchronous machine connected to an infinite bus.

• 한국경영과학회지
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• 제23권1호
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• pp.1-16
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• 1998

The zone clustering problem arising from several area such as deciding the optimal location of ambient measuring stations is to devide the 2-dimensional area into several sub areas in which included individual zone shows simimlar properties. In general, the optimal solution of this problem is very hard to obtain. Therefore, instead of finding an optimal solution, the generation of near optimal solution within the limited time is more meaningful. In this study, the combination of a genetic algorithm and the modified k-means method is used to obtain the near optimal solution. To exploit the genetic algorithm effectively, a representation of chromsomes and appropriate genetic operators are proposed. The k-means method which is originally devised to solve the object clustering problem is modified to improve the solutions obtained from the genetic algorithm. The experiment shows that the proposed method generates the near optimal solution efficiently.