• 한국정보통신학회논문지
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• 제14권2호
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• pp.287-295
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• 2010

본 논문에서는 MPI(Message Passing Interface) 환경 하에서 채널배선 문제에 대한 분산 평균장 유전자 알고리즘(MGA, Mean field Genetic Algorithm)이라는 새로운 최적화 알고리즘을 제안한다. 분산 MGA는 평균장 어닐링(MFA, Mean Field Annealing)과 시뮬레이티드 어닐링 형태의 유전자 알고리즘(SGA, Simulated annealing-like Genetic Algorithm)을 결합한 경험적 알고리즘이다. 평균장 어닐링의 빠른 평형상태 도달과 유전자 알고리즘의 다양하고 강력한 연산자를 합성하여 최적화 문제를 효율적으로 해결하였다. 제안된 분산 MGA를 VLSI 설계에서 중요한 주제인 채널 배선문제에 적용하여 실험한 결과 기존의 GA를 단독으로 사용하였을 때보다 최적해에 빠르게 도달하였다. 또한 분산 알고리즘은 순차 알고리즘에서의 최적해 수렴 특성을 해치지 않으면서 문제의 크기에 대하여 선형적인 수행시간 단축을 나타냈다.

• 한국초지조사료학회지
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• 제39권3호
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• pp.189-194
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• 2019

The normal feeding approach of goats might be due to their precise anatomical and physiological characteristics of entity, which permit them to be highly selective, to eat legume silages and wild green grass. This review has been designed to consider the grazing behavior, fodder selection, and feed composition of goats. Various herbs and corns consumed by goats have numerous nutritive resources. Based on the general herbaceous intake activities and behavior of goats, they prefer wild grass such as grass grown in the steep hills than soft grass. Because the digestion capacity of cellulose feed has higher digestion level compared to other non-ruminants within rumen and it is advantageous to use wild forest or mountain grass which comprises high proportion of cellulose feed for goat. In South Korea, there are abundant feed resources for goats because of occupying large areas of mountains. Thus, goat production and feeding costs could be reduced if plants are used from the wild forest as a feed for goats relative to grassland grazing. Also, it is expected to contribute in improvement of goat farming with harmonious relationship between the grassland and wild forest while satisfying animal welfare and physiological desires of livestock.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.97-108
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• 2001

Frauds detection is a difficult problem, requiring huge computer resources and complicated search activities Researchers have struggled with the problem. Even though a fee research approaches have claimed that their solution is much better than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds. a novel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new self of decision-makin rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Journal of Electrical Engineering and Technology
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• 제3권3호
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• pp.320-330
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• 2008

This paper presents an improved dynamic programming (IDP) approach to solve the economic power dispatch problem including transmission losses in power systems. A detailed mathematical derivation of recursive dynamic programming approach for the economic power dispatch problem with transmission losses is presented. The transmission losses are augmented with the objective function using price factor. The generalized expression for optimal scheduling of thermal generating units derived in this article can be implemented for the solution of the economic power dispatch problem of a large-scale system. Six-unit, fifteen-unit, and forty-unit sample systems with non-linear characteristics of the generator, such as ramp-rate limits and prohibited operating zones are considered to illustrate the effectiveness of the proposed method. The proposed method results have been compared with the results of genetic algorithm and particle swarm optimization methods reported in the literature. Test results show that the proposed IDP approach can obtain a higher quality solution with better performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.33.1-33
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• 2001

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.

• 경영과학
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• 제18권2호
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• pp.117-124
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• 2001

This paper presents a machining parameter selection approach using an evolutionary computation (EC). In order to perform a successful material cutting process, the engineer is to select suitable machining parameters. Until now, it has been mostly done by the handbook look-up or solving optimization equations which is inconvenient when not in handy. The main thrust of the paper is to provide a handy machining parameter selection approach. The EC is applied to rapidly find optimal machining parameters for the user\\`s specific machining conditions. The EC is basically a combination of genetic a1gorithm and microcanonical stochastic simulated annealing method. The approach is described in detail with an application example. The paper concludes with a discussion on the potential of the proposed approach.

• 산업경영시스템학회지
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• 제31권1호
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• pp.101-107
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• 2008

This paper considers a topological optimization of a computer network design with a reliability constraint. The objective is to find the topological layout of links, at minimal cost, under the constraint that the network reliability is more than a given reliability. To efficiently solve the problem, a scatter search approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a genetic algorithm approach.

• 한국산업정보학회논문지
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• 제19권4호
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• pp.87-104
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• 2014

본 연구에서는 지역적으로 집중화된 역물류네트워크(Regionally centralized multistage reverse logistics network: cmRL)와 지역적으로 분산회된 역물류네트워크(Regionally decentralized multistage reverse logistics network: dmRL)를 제안하고 있다. cmRL과 dmRL 각각은 고려되는 영역 전체와 지역적으로 분산된 세부영역에서의 RL 네트워크로 구성된다. 이들은 혼합정수계획법(Mixed integer programming: MIP) 모델로 공식화되며, 유전알고리즘(Genetic algorithm: GA)을 통해 해를 구하게 된다. 사례연구에서는 두 가지 형태의 RL네트워크를 제시하며 다양한 수행도 척도를 사용하여 cmRL과 dmRL의 효율성을 비교분석하였다. 분석결과 cmRL이 dmRL 보다 더 우수한 수행도를 나타내었다.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.448-453
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• 2008

This study describes design and development techniques of estimation models for process modeling. One case study is undertaken to design a model using standard gas furnace data. Neural networks (NN) and genetic programming (GP) are each employed to model the crucial relationships between input factors and output responses. In the case study, two models were generated by using 70% training data and evaluated by using 30% testing data for genetic programming and neural network modeling. The model performance was compared by using RMSE values, which were calculated based on the model outputs. The average RMSE for training and testing were 0.8925 (training) and 0.9951 (testing) for the NN model, and 0.707227 (training) and 0.673150 (testing) for the GP model, respectively. As concern the results, the NN model has a strong advantage in model training (using the all data for training), and the GP model appears to have an advantage in model testing (using the separated data for training and testing). The performance reproducibility of the GP model is good, so this approach appears suitable for modeling physical fabrication processes.