• 대한기계학회논문집A
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• 제25권12호
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2010년도 한국컴퓨터종합학술대회논문집 Vol.37 No.1(A)
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• pp.130-133
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• 2010

본 논문에서는 유전 알고리즘의 성능 향상을 위해 교배(Crossover) 기법의 중요 매개변수인 교배 교차점(Crossover Point)의 수를 개체군(Population)의 진화 과정 중에 적응적으로 변화 할 수 있는 자기-적응형(Self-Adaptive) 교배 기법을 제안한다. 이를 위해 제안 교배 기법은 전체 개체군을 다수개의 작은 개체군들로 군집화(Grouping)하여 일차적으로 서로 다른 교차점을 갖는 교배 기법을 적용시키고, 그 후 각 군집의 개체(Individual)들의 선택률을 기반으로 군집들간의 경쟁을 수행한다. 이는 유전 알고리즘이 개체군의 진화 과정 중에 문제에 적합한 교차점을 갖는 교배 기법을 적응적으로 사용할 수 있도록 한다. 또한 제안 교배 기법은 진화 과정 중에 교차점이 지속적으로 변화되므로 알고리즘 초반에는 높은 탐색 능력을 보유하게 되고 후반에는 높은 부분-해(Building-Block) 보존 능력을 지니게 되어, 최적 해(Optimal Solution)로의 수렴 능력이 향상된다. Deceptive 문제를 통해 제안 자기-적응형 교배 기법과 기존 (고정 교차점) 교배 기법의 성능을 비교 하였으며, 실험 결과로부터 제안 교배의 성능 우위를 확인하였다.

• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.39-50
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• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.254-254
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• 2000

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy (or evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• 한국정보과학회논문지:시스템및이론
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• 제31권1_2호
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• pp.59-66
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• 2004

본 논문에서는 2차원 영상에서 3차원 깊이정보를 추출하기 위해서 진화연산 알고리즘을 적용한 고속 3차원 모델 추출 기법을 제안한다. 진화연산 알고리즘은 자연 선택과 개체군 유전학에 기반 한 생물학적 진화 과정을 통해 최적의 해를 찾는 효율적인 탐색 기법이다. 기존의 스테레오 정합 방법에서 생성되어진 2차원 깊이 정보인 변이 맵은 경계 부근에서 애매한 결과를 도출함으로써 변이의 세밀하고 정확한 정보를 잃어 실 영상과는 다소 차이를 갖는다. 본 논문에서는 소형 유전자 알고리즘을 스테레오 정합환경에 맞게 변형시키고, 생성된 변이 맵의 모호성을 해결하기 위해 이전 세대의 변이 맵으로부터 경계를 검출한 변이 경계정보에서 이웃한 화소의 변이 복잡도를 측정하여 복잡도에 따라 적응적 윈도우를 결정하여 정합에 사용하였다. 실험을 통해 제안한 방식이 이완 처리를 포함한 기존의 정합 방식보다 변이 맵 생성에 있어 보다 상세하고 매끄러운 변이 결과를 얻을 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.1-20
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• 2018

Service composition in the Inter-Cloud raises new challenges that are caused by the different Quality of Service (QoS) requirements of the users, which are served by different geo-distributed Cloud providers. This paper aims to explore how to select and compose such services while considering how to reach high efficiency on cost and response time, low network latency, and high reliability across multiple Cloud providers. A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to get the better convergence and diversity. At the same time, a Local Search (LS) method is performed for the Non-dominated solution set F{1} in each generation to improve the distribution of the F{1}. The simulation results show that our proposed algorithm performs well in terms of the solution distribution and convergence, and in addition, the optimality ability and scalability are better compared with those of the other algorithms.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• International Journal of Fluid Machinery and Systems
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• 제8권3호
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• pp.209-219
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• 2015

A robust multi-fidelity optimization methodology has been developed, focusing on efficiently handling industrial runner design of hydraulic Francis turbines. The computational task is split between low- and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a physics-based surrogate optimization loop manages a large number of iterative optimization evaluations. Two derivative-free optimization methods use an inviscid flow solver as a physics-based surrogate to obtain the main characteristics of a good design in a relatively fast iterative process. The case study of a runner design for a low-head Francis turbine indicates advantages of integrating two derivative-free optimization algorithms with different local- and global search capabilities.