• Journal of Korea Society of Industrial Information Systems
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• v.3 no.1
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• pp.101-116
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• 1998

혼합·이산 비선형 최적화문제 해결을 위한 전역적 최적화 알고리즘이 개발되었으며 이 알고리즘은 확률적 최적화기법인 유전알고리즘을 사용한다. 유전알고리즘은 다양한 설계변수를 처리하는데 적합하다. 그러나 기존의 유전알고리즘이 특별히 잘 수행되지 않는 상황이 많이 존재하기 때문에 혼합화에 대한 다앙한 방법이 개발되어지고 있다. 따라서 이 논문은 유전알고리즘에서 최적해 주위에 대한 국고수수렴기법과 정밀 탐색법을 구체화시킨 새로운 혼합유전알고리즘(NHGA)을 개발했다. 사례연구에서는 혼합·이산 비선형 최적화문제를 해결하는데 있어서 NHGA가 상당한 능력을 제공하며 효율적이고 우수한 해를 제공할 수 있다는 것을 보여주고 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.9-11
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• 2000

전형적인 단순 유전자 알고리즘은 한 개의 모집단으로 구성되며, 진화 과정이 거듭되면 모집단의 개체들은 한 개의 전역해로 수렴하게 된다. 그러나, 많은 문제들은 여러 개의 최적해를 가질 수 있으며, 그것들 모두를 찾는 것이 중요한 경우가 많다. 이 논문에서는 모집단을 원환체(Torus)로 구성하고 개체에 이웃의 개념을 부여하여 모집단이 최적해 집단으로 수렴하는 유전자 알고리즘의 변형을 연구한다. 제안한 방법은 개체사이에 이웃이라는 개념을 부여함으로써 다수의 해를 동시에 찾는다는 생각을 넘어서 다양한 변형 유전자 알고리즘에 대한 새로운 모델이 될 것으로 기대된다.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.87.1-87.1
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• 2005

• Journal of KIISE
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• v.41 no.12
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• pp.1090-1098
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• 2014

In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.71-79
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• 1998

This paper introduces a new optimization algorithm which is combined with genetic algorithm and random tabu search method. Genetic algorithm is a random search algorithm which can find the global optimum without converging local optimum. And tabu search method is a very fast search method in convergent speed. The optimizing ability and convergent characteristics of a new combined optimization algorithm is identified by using a test function which have many local optimums and an optimum allocation of pipe support. The caculation results are compared with the existing genetic algorithm.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.537-542
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• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.305-313
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• 2002

The genetic algorithm(GA), an optimization technique based on the theory of natural selection, has proven to be relatively robust means to search for global optimum. It is converged near to the global optimum point without auxiliary information such as differentiation of function. When studying some optimization problems with continuous variables, it was found that premature saturation was reached that is no further improvement in the object function could be found over a set of iterations. Also, the general GA oscillates in the region of the new global optimum point so that the speed of convergence is decreased. This paper is to propose the concept of restarting and elitist preserving strategy as a measure to overcome this difficulty. Some benchmark examples are studied involving 3-bar truss and cantilever beam with plane stress elements. The modifications to GA improve the speed of convergence.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.590-600
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• 2008

This paper concerns the development of a designer friendly hull form parameterization and its coupling with advanced global optimization algorithms. As optimization algorithms, we choose the Partial Swarm Optimization(PSO) recently introduced to solve global optimization problems. Most general-purpose optimization softwares used in industrial applications use gradient-based algorithms, mainly due to their convergence properties and computational efficiency when a relatively few number of variables are considered. However, local optimizers have difficulties with local minima and non-connected feasible regions. Because of the increase of computer power and of the development of efficient Global Optimization (GO) methods, in recent years nongradient-based algorithms have attracted much attention. Furthermore, GO methods provide several advantages over local approaches. In the paper, the derivative-based SQP and the GO approach PSO are compared with their relative performances in solving some typical ship design optimization problem focusing on their effectiveness and efficiency.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.248-254
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• 2009

The use of evolutionary algorithm is limited in the field of aerodynamics, mainly because the population-based search algorithm requires excessive CPU time. In this paper a coupling method with adaptive range genetic algorithm for floating point and back-propagation neural network is proposed to efficiently obtain a converged solution. As a result, it is shown that a reduction of 14% and 33% respectively in wave drag and its consumed time can be achieved by the new method.