• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.225-228
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• 2001

DNA computing has been applied to the problem of getting an optimal solution since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems. This paper presents DNA coding method for finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms (GA). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses a tool of calculation or Information store with DNA molecules and four-type bases denoted by the symbols of A(Ademine), C(Cytosine), G(Guanine) and T(Thymine). The same operators, selection, crossover, mutation, are applied to the both DNA coding algorithm and genetic algorithms. The results show that the DNA based algorithm performs better than GA.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.112-117
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• 1997

This paper presents a GA(Genetic Algorithms)-Optical control strategy for the control of the swing motion and the transverse position of the overhead crane. The overhead crane system is defined uncertain due to unknown system parameters such as payload and trolly mass. To control the overhead crane. the GA-Optimal control scheme is suggested. which transfers a trolly to a desired place as fast as possible and minimizes the swing of the payload during the transfer. The genetic algorithms are applied to fine digital optimal feedback gains. A computer simulation demonstrate the performance of the proposed the GA-digital optimal controller for the overhead crane.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.71-73
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• 1995

This paper presents an optimization technique using genetic algorithms(GA) for loss minimization in the distribution network reconfiguration. Determining switch position to be opened for loss minimization in the radial distribution system is a discrete optimization problem. GA is appropriate to solve the multivariable optimization problem and it uses population, not a solution. For this reason, GA is attractive to solve this problem. In this paper, we aimed at finding appropriate open sectionalizing switch position using GA, which can lead to minimum transmission losses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.488-490
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• 1995

This paper presents an optimization technique using genetic algorithms(GA) for loss minimization in the distribution network reconfiguration. Determining switch position to be opened for loss minimization in the radial distribution system is a discrete optimization problem. GA is appropriate to solve the multivariable optimization problem and it uses population, not a solution. For this reason, GA is attractive to solve this problem. In this paper, we aimed at finding appropriate open sectionalizing switch position using GA, which can lead to minimum transmission losses.

• 한국전산구조공학회논문집
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• 제16권4호
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• pp.407-417
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• 2003

최근 구조최적화분야에서 활발하게 사용되고 있는 유전알고리즘은 해집단을 운용하기 때문에, 많은 반복수와 적응도 평가를 위하여 해집단의 수에 해당하는 구조해석을 필요로 하며, 또한 교배율과 돌연변이율 등의 파라미터에 따라 알고리즘의 성능이 변화하므로 문제에, 따라 적합한 파라미터 설정이 필요한 근본적인 단점을 지니고 있다. 본 연구에서는 기존 유전알고리즘의 단점을 극복할 수 있는 복합유전알고리즘을 마이크로유전알고리즘과 단순유전알고리즘을 결합한 형식으로 그리고, 최적화에 요구되는 연산을 다수의 개인용 컴퓨터에서 동시에 분산하여 수행할 수 있는 고성능 분산 복합유전알고리즘으로 개발하였다. 개발된 알고리즘은 철골 가새골조 구조물의 최소중량설계에 적용하여 그 성능을 평가하였다.

• 대한전기학회논문지:전력기술부문A
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• 제49권5호
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• pp.212-219
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• 2000

This paper deals with a systematic approach to GA-PI controller design for static VAR compensator(SVC) using genetic algorithms(GAs) which are search algorithms based on the mechanics of natural of natural selection and natural genetics, to improve system stability. A SVC, one of the Flexible AC Transmission System(FACTS), constructed by a fixed capacitor(FC) and a thyristor controlled reactor(TCR), is designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage. To verify the robustness of the proposed method, considered dynamic response of generator used deviation and generator terminal voltage by applying a power fluctuation and three-phase fault at heavy load, normal load and light load. Thus, we proved usefulness of GA-PI controller design to improve the stability of single machine-infinite bus with SVC system.

• 항공우주시스템공학회지
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• 제7권1호
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• pp.8-18
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• 한국정보처리학회논문지
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• 제6권2호
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• pp.326-334
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• 1999

컴퓨터 시스템의 성능을 분석하고 평가하는 방법에는 결함허용(fault tolerance)과 결함회피(fault avoidance) 기법이 있다. 소프트웨어 신뢰성을 향상시키기 위하여 소프트웨어 결함허용 기법 중에서 가장 객관적이고 정량적으로 평가받는 것이 NVP(N-version Programming)기법이다. 이 기법에서 신뢰도를 추정하는 모델로 이항분포를 사용하는데 이 추정 모델은 각 컴포넌트의 신뢰도의 값들이 동일하다는 한계점이 있었다. 본 논문에서는 기존모델의 문제점을 해결하기 위하여 GA (Genetic Algorithms)를 적용하였다. GA를 적용하여 최적화 시뮬레이터를 구현하고 시뮬레이션을 수행해서 비교 분석 및 평가하였다. 그 결과 전체 시스템의 신뢰도를 일정 수준 이상 유지하면서 각 컴포넌트 신뢰도를 최적화 할 수 있었고, 도한 시스템 신뢰도에 가장 적합한 최적의 수를 추정할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.597-605
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• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.383-386
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• 2002

In this paper, we investigated the pattern recognition performance of the numeric patterns (from 0 to 9) using DNA coding method. The pattern recognition performance of the DNA coding method is compared to the that of the GA(Genetic Algorithm). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by A(Adenine), C(Cytosine), G(Guanine) and T(Thymine), The pattern recognition performance of GA and DNA coding method is evaluated by using the same genetic operators(crossover and mutation) and the crossover probability and mutation probability are set the same value to the both methods. The DNA coding method has better characteristics over genetic algorithms (GA). The reasons for this outstanding performance is multiple possible solution presentation in one string and variable solution string length.