• Proceeding of KASS Symposium
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• 2005.05a
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• pp.236-245
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• 2005

This paper was developed the discrete optimum design program by the refined fuzzy-genetic algorithms based on the genetic algorithms and fuzzy theory. The optimum design of this paper can perform both size and shape optimum design for planar and spacial steel structures. In this paper, the objective function is the weight of steel structures and the constraints are the design limits defined by design and buckling strengths, displacements and thicknesses. The design variables are dimensions and coordinates of steel sections. Design examples are given to show the applicability of the discrete optimum design program of this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.370-370
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• 2000

In this paper, we design a Multi-Fuzzy model by means of HCM clustering and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy ate identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy mode] and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.106-116
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• 1999

The purpose of this study is to present a method for determining the k most vital arcs in the maximum flow problem using genetic algorithms. Generally, the problem which determine the k most vital arcs in maximum flow problem has known as NP-hard. Therefore, in this study we propose a method for determining all the k most vital arcs in maximum flow problem using genetic algorithms. First, we propose a genetic algorithm to find the k most vital arcs removed at the same time and then present the expression and determination method of individuals compatible with the characteristics of the problem, and specify the genetic parameter values of constitution, population size, crossover rate, mutation rate and etc. of the initial population which makes detecting efficiency better. Finally, using the proposed algorithms, we analyzed the performance of searching optimal solution through computer experiment. The proposed algorithms found all alternatives within shorter time than other heuristic methods. The method presented in this study can determine all the alternatives when there exists other alternative solutions.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.417-422
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• 2007

A new evolution method termed queen-bee and mutant-bee evolution is based on the previous queen-bee evolution [1]. Even though the queen-bee evolution has shown very good performances, two parameters for strong mutation are added to the genetic algorithms. This makes the application of genetic algorithms with queen-bee evolution difficult because the values of the two parameters are empirically decided by a trial-and-error method without a systematic method. The queen- bee and mutant-bee evolution has no this problem because it does not need additional parameters for strong mutation. Experimental results with typical problems showed that the queen-bee and mutant-bee evolution produced nearly similar results to the best ones of queen-bee evolution even though it didn't need to select proper values of additional parameters.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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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.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.73-81
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• 1996

This paper presents a general procedure for the selection of the machining parameters for a given machine which provides the maximum material removal rate using a Genetic Algorithms(GAs). Some constraints were given in order to achieve desired surface integrity and cutting tool life conditions as wel as to protect machine tool. Such a constrained problem can be transformaed to unconstrained problem by associating a penalty with all constraint violations and the penalties are included in the function evaluation. Genetic Algorithms can be used for finding global optimum cutting conditions with respect to the above cost function transformed by pennalty function method. From the demonstration of the numerical results, it was found that the near optimal conditions could be obtained regardless of complex solution space such as cutting environment.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.515-518
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• 1998

This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using 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 algorithms. Simulation results verify the validity of the presented method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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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.