• KSBB Journal
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• 제14권4호
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• pp.495-502
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• 1999

The optimization of fed-batch yeast fermentation process has been performed using genetic algorithm(GA). Three strategies were designed and applied to obtain the optimal feed rate profiles. Genes in the chromosome (input variables for optimization) included feed rates on fixed time intervals (strategy I), or swiching times $t_s1\;and\;t_s2$, and feed rates on singular arc (strategy II), or feed rates and the length of time interval (strategy III). Strategy III showed the best results for all initial conditions due to efficient utilization of genetic information. Simulation results using GA showed similar or better performance compared with previous results by variational caculus and singular control approach.

• 수산해양교육연구
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• 제29권2호
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• pp.544-551
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• 2017

Metaheuristic algorithms are efficient techniques for a class of mathematical optimization problems without having to deeply adapt to the inherent nature of each problem. They are very useful for structural design optimization in which the cost of gradient computation can be very expensive. Among them, the characteristics of simulated annealing and genetic algorithms are briefly discussed. In Metropolis genetic algorithm, favorable features of Metropolis criterion in simulated annealing are incorporated in the reproduction operations of simple genetic algorithm. Numerical examples of structural design optimization are presented. The example structures are truss, breakwater and steel box girder bridge. From the theoretical evaluation and numerical experience, performance and applicability of metaheuristic algorithms for structural design optimization are discussed.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.587-592
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by simplex method in reconstructive search space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.131-138
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• 2022

A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.

• 대한토목학회논문집
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• 제32권6A호
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• pp.369-378
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• 2012

이 연구에서는 풍력 터빈 블레이드의 형상 최적화를 위한 직접탐색 기반의 최적화 기법을 적용하고, 최적화 기법간의 성능을 비교하여 효과적인 방법을 제안하고자 하였다. 이를 위하여 수평축 풍력 터빈의 최적설계 코드인 HARP_Opt(Horizontal Axis Rotor Performance Optimizer)을 기반으로 연간 발전량 평가 방법을 수정하고, HARP_Opt에서 적용하고 있는 기존의 유전자 알고리즘과 함께 패턴 서치 방법을 추가 적용하였다. 이를 1MW급 풍력 발전 터빈 블레이드의 단면 형상 최적 설계 문제에 적용하였으며, 기존의 유전자 알고리즘 및 마이크로 유전자 알고리즘, 그리고 패턴 서치 방법의 성능을 비교한 결과, 연간 발전량과 해의 일관성 면에 있어서는 패턴 서치 방법이 상대적으로 우수하였으며, 계산시간 측면에서는 마이크로 유전자 알고리즘이 상대적으로 우수한 것으로 분석되었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Composites Research
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• 제23권4호
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• pp.14-20
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• 2010

본 연구에서는 마이크로 유전자 알고리즘을 이용하여 외부 수압을 받는 필라멘트 와인딩 복합재 원통의 최적설계를 수행하였다. 목적함수는 파손하중과 좌굴하중을 동시에 고려하여 설계하중을 최대화하는 것이다. 좌굴 및 파손해석은 MSC.NASTRAN을 이용하였고, Carroll의 공개된 마이크로 유전자 알고리즘에 기초한 최적화작업을 수행하였다. 설계변수로는 헬리컬(helical) 와인딩 각도와 후프(hoop) 와인딩 층의 두께비가 고려되었다. 본 연구를 통해 마이크로 유전자 알고리즘을 이용하여 다양한 형상을 갖는 필라멘트 와인딩 복합재 원통의 좌굴 및 파손하중 최적화가 가능함을 확인하였고, 제안된 알고리즘이 일반 유전자 알고리즘과 비교해서도 높은 효율을 보였다.

• 대한기계학회논문집A
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• 제28권10호
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• pp.1558-1565
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• 2004

It is important to determine supporting locations for structural stability when a structure is loaded with non-uniform load or supporting locations as well as the number of the supporting structures are restricted by the problem of space. Moreover, the supporting location optimization of complex structure in real world is frequently faced with discontinuous design space. Therefore, the traditional optimization methods based on derivative are not suitable Whereas, Genetic Algorithm (CA) based on stochastic search technique is a very robust and general method. The KSTAR in-vessel control coil installed in vacuum vessel is loaded with non- uniform electro-magnetic load and supporting locations are restricted by the problem of space. This paper shows the supporting location optimization for structural stability of the in-vessel control coil. Optimization has been performed by means of a developed program. It consists of a Finite Element Analysis interfaced with a Genetic Algorithm. In addition, this paper presents an algorithm to find an optimum solution in discontinuous space using continuous design variables.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
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• pp.165.1-165.1
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• 2005

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2006년도 PARALLEL CFD 2006
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• pp.393-394
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• 2006