• Proceedings of the KSME Conference
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• 2001.06a
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• pp.752-757
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• 2001

To reduce stress concentration around the intersection between a spherical pressure vessel and a cylindrical nozzle under various load conditions using less material, the optimization for the distribution of reinforcement has researched. The ranked bidirectional evolutionary structural optimization(R-BESO) method is developed recently, which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO. In this paper, the optimized intersection shape is sought using R-BESO method for a flush and a protruding nozzle. The considered load cases are a radial compression, torque and shear force.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1444-1451
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• 2001

The evolutionary structural optimization(ESO) method has been under continuous development since 1992. The bidirectional evolutionary structural optimization(BESO) method is made of additive and removal procedure. The BESO method is very useful to search the global optimum and to reduce the computational time. This paper presents the ranked bidirectional evolutionary structural optimization(R-BESO) method which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.99-103
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• 2007

본 논문은 최적 탐색 알고리즘중의 하나인 실수 표현 진화 알고리즘에 자가 적용 세대차 조절을 이용하여 보다 빠른 연산으로 우수해에 접근하기 위한 새로운 방식을 소개한다. 알고리즘의 성능에 영향을 끼치는 진화 속도를 기존 진화 방식과 유전연산자의 수정을 통해 조절하여 탐색 성능을 개선 한다. 조기 수렴의 방지 및 탐색성능의 향상을 위하여 선택과 대치를 포함한 진화방식을 개선하고, 유전 연산자에 의하여 생성된 자손의 대치확률에 따라서 자손의 생성범위를 자가 적응적으로 조절하여, 보다 적은 계산량으로 전역 최적화를 찾고자 한다. 제안된 방법을 벤치마크 테스트 문제에 적용하여 G3 알고리즘, CMA-ES 그리고 DE 등과 성능을 비교하였다.

• Annual Conference of KIPS
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• 2009.11a
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• pp.97-98
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• 2009

진화 전략은 생식, 돌연변이, 재조합과 같은 생물의 진화과정을 모델링하여 복잡한 문제를 해결하고자 하는 개체군 기반의 조합 최적화 알고리즘 중의 하나이다. 데이터 집약적이며, 소요 시간이 오래 걸리는 진화 전략은 클라우드 컴퓨팅 하의 IT 서비스로서 적합한 대표적인 예이다. 이에 본 논문에서는 최근 분산 환경 하에서 병렬 처리 응용을 쉽게 개발할 수 있도록 지원하는 프로그래밍 모델인 MapReduce 를 확장하여 진화 전략을 수행할 수 있는 방법을 제안한다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.1-9
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• 2014

With the development of quantum computer, the development of the quantum-inspired search method applying the features of quantum mechanics and its application to engineering problems have emerged as one of the most interesting research topics. This algorithm stores information by using quantum-bit superposed basically by zero and one and approaches optional values through the quantum-gate operation. In this process, it can easily keep the balance between the two features of exploration and exploitation, and continually accumulates evolutionary information. This makes it differentiated from the existing search methods and estimated as a new algorithm as well. Thus, this study is to suggest a new minimum weight design technique by applying quantum-inspired search method into structural optimization of planar truss. In its mathematical model for optimum design, cost function is minimum weight and constraint function consists of the displacement and stress. To trace the accumulative process and gathering process of evolutionary information, the examples of 10-bar planar truss and 17-bar planar truss are chosen as the numerical examples, and their results are analyzed. The result of the structural optimized design in the numerical examples shows it has better result in minimum weight design, compared to those of the other existing search methods. It is also observed that more accurate optional values can be acquired as the result by accumulating evolutionary information. Besides, terminal condition is easily caught by representing Quantum-bit in probability.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.278-286
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• 2020

The prisoner's dilemma game which is a representative example of game theory is being studied with interest by many economists, social scientists, and computer scientists. In recent years, many researches on computational approaches that apply evolutionary computation techniques such as genetic algorithms and particle swarm optimization have been actively conducted to analyze prisoner dilemma games. In this study, we intend to evolve a strategy for a iterated prisoner dilemma game participating two or more players using three different binary particle swarm optimization techniques. As a result of experimenting by applying three kinds of binary particle swarm optimization to the iterated prisoner's dilemma game, it was confirmed that mutual cooperation can be established even among selfish participants to maximize their own gains. However, it was also confirmed that the more participants, the more difficult to establish a mutual cooperation relationship.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.685-689
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• 2009

Evolutionary Computation is not only widely used in optimization and machine learning, but also being applied in creating novel structures and entities. This paper proposes evolutionary topic maps that can suggest new and creative knowledge not easily producible by humans. Interactive evolutionary computation method is applied into topic maps in order to accept human evaluation on feasibility of intermediate topic maps. Evolutionary topic maps are creativity support tools, helping users to encounter new and creative knowledge. Further work can greatly improve the system by providing more operations, preventing over-convergence, and overcoming user fatigue problem by providing more intuitive user interface, better visualization, and interpolation mechanisms.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1071-1083
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• 2005

By estimating probability distributions of the good solutions in the current population, some researchers try to find the optimal solution more efficiently. Particularly, finite mixtures of distributions have a very useful role in dealing with complex problems. However, it is difficult to choose the number of components in the mixture models and merge superior partial solutions represented by each component. In this paper, we propose a new continuous evolutionary optimization algorithm with distribution estimation by variational Bayesian mixtures of factor analyzers. This technique can estimate the number of mixtures automatically and combine good sub-solutions by sampling new individuals with the latent variables. In a comparison with two probabilistic model-based evolutionary algorithms, the proposed scheme achieves superior performance on the traditional benchmark function optimization. We also successfully estimate the parameters of S-system for the dynamic modeling of biochemical networks.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.6
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• pp.726-731
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• 2004

This paper suggests a material mixing method to mix several materials in a structure. This method is based on ESO(Evolutionary Structural Optimization), which has been used to optimize topology of only one material structure. In this study, two criterions for material transformation and element removal are implemented for mixing several materials in a structure. Optimal topology for a multiple material structure can be obtained through repetitive application of the two criterions at each iteration. Two practical design examples of a short cantilever are presented to illustrate validity of the suggested material mixing method. It is found that the suggested method works very well and a multiple material structure has more stiffness than one material structure has under the same mass.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.4
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• pp.50-56
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• 2003

Most engineering products contain more than one component. Failure occurs either at the connection itself or in the component at the point of attachment of the connection in many engineering structures. The allocation and design of connections such as bolts, spot-welds, adhesive etc. usually play an important role in the structure of multi-components. Topology optimization of connection component provides more practical solution in design of multi-component connection system. In this study, a topology optimization based on density distribution approach has been applied to optimal location of fasteners such as T-shape, L-shape and multi-component connection system. From the results, it was verified that the number of iteration was reduced, and the optimal topology was obtained very similarly comparing with ESO method. Therefore, it can be concluded that the density distribution method is very suitable for topology optimization of multi-component structures.