• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.53-68
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• 1999

To improve the slow convergence property of the steepest ascent type algorithm for continuous D-optimal design problems. we develop a new algorithm. We apply the nonlinear system of equations as the necessary condition of optimality and develop the two-point algorithm that solves the problem of clustering. Because of the nature of the steepest coordinate ascent algorithm avoiding the problem of clustering itself helps the improvement of convergence speed. The numerical examples show the performances of the new method is better than those of various steepest ascent algorithms.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.622-624
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• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.468-471
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• 2003

The A＊ algorithm is usually used in game programming, mainly because it is fast in finding a optimal path to goal. In this paper. This algorithm was utilized for path finding, HIMM(Histogramic In-Motion Mapping) method is used in map-building. Map is updated continuously with range data sampled by PSD sensors From the map, A＊ algorithm finds a optimal path and sends subsequently the most suitable point to the Eyebot. A＊ algorithm has been tested on the Eyebot in various unknown maps of unknown and proved to work well. It could escape the local minimum, also.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2002년도 추계학술발표회
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• pp.24-27
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• 2002

The optimal designs of groundwater problems of contaminant containment and cleanup using linear programming and genetic algorithm are provided. In the containment problem, genetic algorithm shows the superior feature to linear programming. In cleanup problem, genetic algorithm makes reasonable optimal design. Un this study, it is demonstrated through numerical experiments that genetic algorithm can be applied to remedial designs of groundwater problems.

• 대한원격탐사학회지
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• 제18권5호
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• pp.291-297
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• 2002

Optimal allocation of viewers on a terrain in such a wav that the hidden area would be minimized has many practical applications. However, it is impossible in practical sense to evaluate all the possible allocations. In this paper, we propose an optimal allocation of viewers based on genetic algorithm that enables probabilistic search of huge solution space. An experiment for one and three viewers was performed. The algorithm converges to good solutions. Especially, in one viewer case, the algorithm found the best solution.

• 한국해양공학회지
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• 제20권1호
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• pp.69-76
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• 2006

The design of efficient search path to maximize the Cumulative Detection Probability(CDP) is mainly dependent on experience and intuition when searcher detect the target using SONAR in the ocean. Recently with the advance of modeling and simulation method, it has been possible to access the optimization problems more systematically. In this paper, a method for the optimal search path calculation is developed based on the combination of the genetic algorithm and the calculation algorithm for detection range. We consider the discrete system for search path, space, and time, and use the movement direction of the SONAR for the gene of the genetic algorithm. The developed algorithm, OASPP(Optimal Acoustic Search Path Planning), is shown to be effective, via a simulation, finding the optimal search path for the case when the intuitive solution exists. Also, OASPP is compared with other algorithms for the measure of efficiency to maximize CDP.

• 조명전기설비학회논문지
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• 제25권10호
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• pp.47-58
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• 2011

This paper proposes the optimal current detect(OCD) maximum power point tracking(MPPT) control of photovoltaic(PV) system for robust with environment changing. The output characteristics of the solar cell is a nonlinear and affected by a temperature, the solar radiation and temperature. Conventional MPPT control methods are tracked the maximum power point by constant incremental value. So these methods are slow the response speed and generated the vibration in steady state and cannot track the MPP in environment condition changing. And power loss is generated because of the self-excitation vibration in MPP region. To solve this problem, this paper proposes the novel control algorithm. Proposed algorithm is detected the optimal current in two control region using the output power and current curve. Detected current is used the converter switching for tracking the MPP. Proposed algorithm is compared output power error to conventional algorithm with radiation and temperature changing. In addition, the validity of the algorithm is proved through the output error response characteristics.

• 한국정보통신학회논문지
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• 제8권5호
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• pp.1089-1095
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• 2004

주어진 네트워크에서 최적의 스타이너 트리를 구하는 문제는 NP-hard이며, 최적에 가까운 스타이너 트리를 구하기 위하여 유전자 알고리즘을 이용한다. 본 논문에서는 이 문제를 해결하기 위하여 유전자 알고리즘에서 염색체를 기존의 이진스트링 대신 부동소수점으로 표현하였다. 먼저 주어진 네트워크에 Prim의 알고리즘을 적용하여 스패닝 트리를 구하고, 부동소수점 표현을 갖는 유전자 알고리즘을 사용하여 새로운 스타이너 점을 트리에 추가하는 과정을 반복함으로써 최적에 가까운 스타이너 트리를 구했다 이 방법을 사용하면 이진스트링을 사용하는 기존의 방법에 비해서 트리가 보다 빠르고 정확하게 최적에 가까운 스타이너 트리에 접근했다.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.36-43
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• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

• 한국컴퓨터정보학회논문지
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• 제4권1호
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• pp.38-46
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• 1999

선박에서 항해시간을 줄이고 연료소비를 절감하기 위하여 최적항로의 결정하는 작업은 선박운항의 가장 중요한 요소이다. 최근, 보다 빠르고, 정확한 최적항로를 결정하기 위해 전자해도시스템이 개발되고 있다. 본 논문은 이러한 시스템에 탐색의 알고리즘 중 최선의 탐색해를 제공하는 $A^*$알고리즘을 전자해도 시스템에 적용하였다. 그러나 $A^*$알고리즘의 적용은 과도한 탐색시간과 많은 메모리를 요구하는 문제점이 있다는 사실이 발견되었고 이러한 문제점을 해결하기 위해 장애물을 발견했을 때 탐색 후보 선정에 가중치를 부여하는 보다 개선된 알고리즘의 필요성을 제안하고자 한다.