• 제목/요약/키워드: 유전자알고리듬

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유전자 알고리듬을 이용한 공자기계구조물의 정강성 해석 및 다목적 함수 최적화(I) (Static Compliance Analysis & Multi-Objective Optimization of Machine Tool Structures Using Genetic Algorithm(I))

  • 이영우;성활경
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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    • pp.443-448
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    • 2000
  • In this paper, multiphase optimization of machine structure is presented. The goal of first step is to obtain (i) light weight, (ii) rigidity statically. In this step, multiple optimization problem with two objective functions is treated using Pareto Genetic Algorithm. Where two objective functions are weight of the structure, and static compliance. The method is applied to a new machine structure design.

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연속 최적화 문제에 대한 수렴성이 개선된 순차적 주밍 유전자 알고리듬 (Convergence Enhanced Successive Zooming Genetic Algorithm far Continuous Optimization Problems)

  • 권영두;권순범;구남서;진승보
    • 대한기계학회논문집A
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    • 제26권2호
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    • pp.406-414
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    • 2002
  • A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

전 영역 그레이코드 유전자 알고리듬의 효율성 증대에 관한 연구 (A Study on Computational Efficiency Enhancement by Using Full Gray Code Genetic Algorithm)

  • 이원창;성활경
    • 한국정밀공학회지
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    • 제20권10호
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    • pp.169-176
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    • 2003
  • Genetic algorithm (GA), which has a powerful searching ability and is comparatively easy to use and also to apply, is in the spotlight in the field of the optimization for mechanical systems these days. However, it also contains some problems of slow convergence and low efficiency caused by a huge amount of repetitive computation. To improve the processing efficiency of repetitive computation, some papers have proposed paralleled GA these days. There are some cases that mention the use of gray code or suggest using gray code partially in GA to raise its slow convergence. Gray code is an encoding of numbers so that adjacent numbers have a single digit differing by 1. A binary gray code with n digits corresponds to a hamiltonian path on an n-dimensional hypercube (including direction reversals). The term gray code is open used to refer to a reflected code, or more specifically still, the binary reflected gray code. However, according to proposed reports, gray code GA has lower convergence about 10-20% comparing with binary code GA without presenting any results. This study proposes new Full gray code GA (FGGA) applying a gray code throughout all basic operation fields of GA, which has a good data processing ability to improve the slow convergence of binary code GA.

재능 유전인자를 갖는 네스티드 유전자 알고리듬을 이용한 새로운 다중 초점 이미지 융합 기법 (A Novel Multi-focus Image Fusion Scheme using Nested Genetic Algorithms with "Gifted Genes")

  • 박대철;론넬 아톨레
    • 한국인터넷방송통신학회논문지
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    • 제9권1호
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    • pp.75-87
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    • 2009
  • 본 논문에서 이미지 선명도 함수의 최적화에 의해 융합 법칙이 유도되는 새로운 이미지 융합 접근법을 제안한다. 선명도 함수에 비교하여 소스 이미지로부터 최적 블록을 통계적으로 선택하기 위하여 유전자 알고리듬이 사용되었다. 변이 연산에 의해 만들어진 유전인자들의 포격을 통해서 찾아진 재능 유전 인자를 갖는 새로운 네스티드 유전자 알고리듬을 설계하였고 구현하였다. 알고리듬의 수렴은 해석적으로, 실험적으로 그리고 통계적으로 3개의 테스트 함수를 사용하여 표준 GA와 비교하였다. 결과의 GA는 변수와 집단 크기에 불변이며, 최소 20 개체이면 시험에 충분하다는 것을 알 수 있었다. 융합 응용에서 모집단내의 각 개체는 입력 블록을 나타내는 유한한 이산 값을 갖는 개체이다. 이미지 융합 실험에 제안한 기법의 성능은 출력 품질 척도로 상호 정보량(MI)으로 특징지워진다. 제안한 방법은 C=2 입력 이미지에 대해 테스트되었다. 제안한 방법의 실험 결과는 현재의 다중 초점 이미지 융합 기법에 대한 실제적이고 매력적인 대안이 됨을 보여준다.

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이동최소자승 중첩 격자 기법과 유전자 알고리듬을 이용한 2차원 구조물의 경감공 위치 최적 설계 (Optimization of Position of Lightening Hole in 2D Structures through MLS basede Overset Metheod along with Genetic Algorithm)

  • 오민환;우동주;조진연
    • 한국항공우주학회지
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    • 제36권10호
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    • pp.979-987
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    • 2008
  • 항공우주 구조물의 설계 시, 과도한 응력집중을 방지하기 위해 경감공의 위치를 변경해야 하는 경우가 종종 발생한다. 이러한 위치 최적 설계를 위해서는 경감공의 위치 갱신에 따라 변경된 구조 형상을 반영할 수 있도록 재 모델링을 수행해야 한다. 널리 사용되는 재 모델링 기법으로는 재 격자 생성기법을 들 수 있다. 그러나 구조물의 형상이 변경될 때마다 격자를 재생성 할 경우 많은 시간이 소요되며, 특히 사면체나 삼각형에 비해 좋은 성능을 가진 육면체나 사각형 격자 사용에 제약이 따르게 된다. 본 논문에서는 이러한 문제점을 보완하기 위해 이동최소자승법 기반의 중첩 격자 기법과 유전자 알고리듬을 이용한 새로운 위치 최적 설계 알고리듬을 제안하였으며, 제안된 위치 최적 설계 알고리듬의 성능을 평가하기 위해 2차원 구조물의 경감공 위치 최적 설계를 수행하였다.

유전자 알고리듬을 이용한 자동차용 Mirror Actuator의 최적설계 (Genetic Algorithm Based Optimal Design for an Automobile Mirror Actuator)

  • 박원호;김재실;최헌오
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2001년도 춘계학술대회논문집C
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    • pp.559-564
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    • 2001
  • The design of an automobile mirror actuator system needs a systematic optimization due to several variables, constraints, geometric limitations, moving angle, and so on. Therefore, this article provides the procedure of a genetic algorithm(GA) based optimization with finite element analysis for design of a mirror actuator considering design constraints, geometric limitations, moving angle. Local optimum problem in optimization design with sensitivity analysis is overcome by using zero-order overall searching method which is new optimization design method using a genetic algorithm.

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유전자 알고리듬을 이용한 동역학적 구조물의 최적설계 (Optimal Design of Dynamic System Using a Genetic Algorithm(GA))

  • 황상문;성활경
    • 한국정밀공학회지
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    • 제16권1호통권94호
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    • pp.116-124
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    • 1999
  • In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.

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유전자 알고리듬을 이용한 공작기계구조물의 정강성 해석 및 다목적 함수 최적화(II) (Static Compliance Analysis & Multi-Objective Optimization of Machine Tool Structures Using Genetic Algorithm(II))

  • 이영우;성활경
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2001년도 추계학술대회(한국공작기계학회)
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    • pp.231-236
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    • 2001
  • The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

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자연요소법과 유전자 알고리듬을 사용한 원공 평판의 최적설계 (Optimization of a Membrane with a Center Hole using Natural Element Method and Genetic Algorithm)

  • 이상범;성활경;천호정
    • 한국정밀공학회지
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    • 제25권2호
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    • pp.105-114
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    • 2008
  • Natural element method (NEM) is quick in research activities by natural sciences and mechanical engineering fields, and from which good results are watched by various engineering fields and applied too. However no paper or research about the applied case has announced yet. Therefore on this paper, I will rediscover an optimum design and apply NEM into other fields with NEM for existing optimum design of mainly using FEM. NEM and genetic algorithm (GA) are applied to optimize a membrane with a center hole. The optimal design obtained by NEM is compared to the counterpart obtained by the finite element method (FEM). Result by NEM is found to be better than the result by FEM. NEM can be a feasible analysis tool in design optimization.

유전자 알고리듬을 이용한 선박용 파이프 경로 최적화 (Ship Pipe Layout Optimization using Genetic Algorithm)

  • 박철우;천호정
    • 한국정밀공학회지
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    • 제29권4호
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    • pp.469-478
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    • 2012
  • This study aims to discover the optimal pipe layout for a ship, which generally needs a lot of time, efforts and experiences. Genetic algorithm was utilized to search for the optimum. Here the optimum stands for the minimum pipe length between two given points. Genetic algorithm is applied to planar pipe layout problems to confirm plausible and efficiency. Sub-programs are written to find optimal layout for the problems. Obstacles are laid in between the starting point and the terminal point. Pipe is supposed to bypass those obstacles. Optimal layout between the specified two points can be found using the genetic algorithm. Each route was searched for three case models in two-dimensional plane. In consequence of this, it discovered the optimum route with the minimized distance in three case models. Through this study, it is possible to apply optimization of ship pipe route to an actual ship using genetic algorithm.