• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1997년도 춘계학술대회논문집
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• pp.155-158
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• 1997

The investigation deals with of a intermediate process condition hving a bolt-shaped final product where it is required to extend tool-life in forging. In this study, optimization of the design variables is conducted by a genetic algorithm, where the fitness values are evaluated on the basis of FEM analysis model. The approach is applied to the determination of the intermediate process conditions which are optimal with regard to minimization of peak die pressure.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2003년도 학술대회논문집
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• pp.355-359
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• 2003

In this paper, an identification method for a first order dead time process is proposed. This method used the genetic algorithm for parameter identification of process. The proposed method gives a better identification result than the existing methods under step testing. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.79-87
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• 2023

본 연구는 조선소 소조립 공정에서 스키드 투입 순서 최적화를 통해 전체 작업시간을 단축시키는 새로운 유전 알고리즘 방법을 제안한다. 하나의 해는 스키드 번호들의 순열로 표현되며 그러한 표현에 적합한 유전 연산자들을 적용하였다. 또한 탐색 성능의 개선을 위해 UniDev라 불리우는 기존의 휴리스틱 알고리즘을 적절하게 변형하여 유전 알고리즘과 결합하였다. 특히 UniDev에서 느린 스키드 탐색 부분을 그리디 알고리즘의 형태로 변경하였다. 매우 큰 규모의 문제에 대해 시뮬레이션을 수행한 결과 Multi-Start 탐색과 UniDev기반 혼합형 유전알고리즘에 비해 본 연구에서 제안하는 방법이 안정적으로 작업시간을 최소화함을 관찰하였다.

• 소음진동
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• 제8권6호
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• pp.1113-1120
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• 1998

For an analysis of some Helmholtz resonators, it is likely to be more appropriate to consider acoustic field within cavity than just the 1-DOF analogous model. However, a design method that considers increased parameters than the lumped model. is not a trivial process due to the trade-off effect among the parameters. In this paper. the genetic algorithm. one of the optimization technique that rapidly converges to global fittest solution and robust convergence. is applied to the design process of Helmholtz resonators. Results show that the genetic algorithm can be successfully and efficiently used to find the resonant frequencies for both lumped model and distributed model.

• 경영과학
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• 제17권2호
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• pp.39-54
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• 2000

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guide genetic algorithm(GGA) is an extended genetic algorithm(GA) using penalty function and population diversity index to increase forecasting accuracy. GGA adds to the canonical GA the concept of a penalty function to avoid selecting the unproductive chromosomes and to make a proper searching direction. Also, GGA modifies the current population using the similarity of chromosomes to avoid falling into the trap of local optimal solution. For investigation GGA performance, we used a set of real data that was collected in local glass melting processes, and experimental results show the proposed model results in the better forecasting accuracy than linear regression model and canonical GA.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.952-955
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• 1996

It is difficult to determine the feeding rate of coagulant in water treatment process, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the fusion of genetic algorithms and fuzzy inference system was used in determining of feeding rate of coagulant. The genetic algorithms are excellently robust in complex operation problems, since it uses randomized operators and searches for the best chromosome without auxiliary information from a population consists of codings of parameter set. To apply this algorithms, we made the look up table and membership function from the actual operation data of water treatment process. We determined optimum dosages of coagulant (PAC, LAS etc.) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• 한국해양공학회지
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• 제24권6호
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• pp.109-113
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• 2010

We present the minimum weight optimum design of cross sectional for frame structures subject to natural frequency. The optimum design in this paper employ discrete and continuous design variables and Genetic Algorithms. In this paper, Genetic Algorithms is used in optimization process, and be used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process. For 1-Bay 2-Story frame structure, in examples, continuous and discrete design variables are used, and W-section (No.1~No.64), from AISC, discrete data are used in discrete optimization. In this case, Exhaustive search are used for finding global optimum. Continuous variables are used for 1-Bay 7-Story frame structure. Two typical frame structure optimization examples are employed to demonstrate the availability of Genetic Algorithms for solving minimum weight optimum of frame structures with fundamental and multi frequency.

• 한국공작기계학회논문집
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• 제16권5호
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• pp.205-210
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• 2007

The integration method of binary and real encoding in genetic algorithm is proposed to deal with design variables of various types in gear drive design. The method is applied to optimum design of multi-stage gear drive. Integer and Discrete type design variables represent the number of teeth and module, and continuous type design variables represent face width, helix angle and addendum modification factor etc. The proposed genetic algorithm is applied for the gear ratio optimization and the volume optimization(minimization) of multi-stage geared motor which is used in field. In result, the proposed design optimization method shows an effectiveness in optimum design process and the new design has a better results compared with the existing design.

• 대한조선학회논문집
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• 제54권5호
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• pp.378-386
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• 2017

In this paper, a method is presented for the optimal design of submarine pressure hull structures by taking advantage of genetic algorithm techniques. The objective functions and design constraints in the process of structural optimization are based on the ultimate limit states of hull structures. One of the benefits associated with the utilization of genetic algorithm is that the optimization process can be completed within short generations of design variables for the pressure hull structure model. Applied examples confirm that the proposed method is useful for the optimal design of submarine pressure hull structures. Details of the design procedure with applied examples are documented. The conclusions and insights obtained from the study are summarized.