• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2000년도 춘계학술발표대회
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• pp.177-180
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• 2000

An algorithm for modeling of yeast fermentation process using genetic-fuzzy algorithm is presented in this work. The algorithm involves developing the fuzzy modeling of the process and model update capability against the system change. The membership functions of state variables and specific rates and the decision table were generated using genetic algorithm. This algorithm could replace the complex mathematical model to simple fuzzy model and cope with the change of process characteristics well.

• 소음진동
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• 제10권1호
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• pp.33-40
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• 2000

With the increased requirement for automobile noise, a design fo mufflers with higher performances becomes more important in recent days. For a design of some mufflers, it must satisfy both minimizing back pressure and maximizing sound attenuation in broad range of frequecny. Even for a simple Helmholtz resonator, an important element in a muffler, a resonator design with accurate resonant frequency is difficult if one want to consider standing waves within the cavity. In this paper, the genetic algorithm, one of the optimization technique with high capability of global fittest solution and robust convergence, is applied to the design process of mufflers. Results show that the genetic algorithm can be successfully and efficiently used to find the fittest model for both mufflers and Helmoltz resonators.

• 산업경영시스템학회지
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• 제26권4호
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• pp.23-30
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• 2003

This study develops an efficient heuristic which is based on genetic approach for AGVs flow path layout problem. The suggested solution approach uses a algorithm to replace two 0-1 integer programming models and a branch-and-bound search algorithm. Genetic algorithms are a class of heuristic and optimization techniques that imitate the natural selection and evolutionary process. The solution is to determine the flow direction of line in network AGVs. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. Genetic algorithm procedure is suggested, and a simple illustrative example is shown to explain the procedure.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.863-868
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• 1992

This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.703-705
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• 1998

In this paper, we have suggested the method of genetic algorithm to solve the trajectory optimization. The given nonlinear method is so complex and modeling is not easy. Also, we have suggested the nonlinear programming combined with genetic algorithm. The proposed algorithm gives simple and time-reducing method in solving nonlinear dynamic systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.820-822
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• 1996

This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.76-82
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• 2001

This paper presents a approach for a service restoration and optimal reconfiguration of distribution network using Genetic algorithm(GA) and Tabu search(TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve in short times, because distribution network supplies power for customers combined with many tie-line switches and sectionalizing switches. Furthermore, the solutions of these problems have to satisfy radial operation conditions and reliability indices. To overcome these time consuming and sub-optimal problem characteristics, this paper applied Genetic-Tabu algorithm. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to complement the weak points of each algorithm. The case studies with 7 bus distribution network showed that not the loss reduction but also the reliability cost should be considered to achieve the economic service restoration and reconfiguration in the distribution network. The results of suggested Genetic-Tabu algorithm and simple Genetic algorithm are compared in the case study also.

• 대한산업공학회지
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• 제28권1호
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• pp.76-86
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• 2002

In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.

• Structural Engineering and Mechanics
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• 제62권2호
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• pp.139-142
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• 2017

For the conventional computational methods for structural reliability analysis, the common limitations are long computational time, large number of iteration and low accuracy. Thus, a new novel method for structural reliability analysis has been proposed in this paper based on response surface method incorporated with an improved genetic algorithm. The genetic algorithm is first improved from the conventional genetic algorithm. Then, it is used to produce the response surface and the structural reliability is finally computed using the proposed method. The proposed method can be used to compute structural reliability easily whether the limit state function is explicit or implicit. It has been verified by two practical engineering cases that the algorithm is simple, robust, high accuracy and fast computation.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.533-538
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• 2001

This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a genetic algorithm and a local concentrate search algorithm (e. g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables.