• Title/Summary/Keyword: 유전자알고리듬

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

  • 이영우;성활경
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2000.10a
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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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Convergence Enhanced Successive Zooming Genetic Algorithm far Continuous Optimization Problems (연속 최적화 문제에 대한 수렴성이 개선된 순차적 주밍 유전자 알고리듬)

  • Gwon, Yeong-Du;Gwon, Sun-Beom;Gu, Nam-Seo;Jin, Seung-Bo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.2
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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.

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

  • 이원창;성활경
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.10
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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.

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

  • Park, Dae-Chul;Atole, Ronnel R.
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.9 no.1
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    • pp.75-87
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    • 2009
  • We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, comparative to the clarity function, the optimum block from among the source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks. Performance of the proposed technique applied to image fusion experiments, is characterized in terms of Mutual Information (MI) as the output quality measure. The method is tested with C=2 input images. The results of the proposed scheme indicate a practical and attractive alternative to current multi-focus image fusion techniques.

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

  • Oh, Min-Hwan;Woo, Dong-Ju;Cho, Jin-Yeon
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.36 no.10
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    • pp.979-987
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    • 2008
  • In aerospace structural design, the position of lightening hole is often required to be optimized from the initial design in order to avoid an excessive stress concentration. To remodel the updated configuration in optimization procedure, re-meshing procedure is conventionally adopted. However, this approach is time-consuming, and has limitations especially in handling hexahedral or quadrilateral meshes, which are preferred because of their good numerical performances. To attenuate these disadvantages, new optimization scheme is proposed by combining the MLS(Moving Least Squares) based overset method and the genetic algorithm in this work. To test the validity of the proposed optimization scheme, optimizations of positions of lightening holes in 2D structures have been carried out.

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

  • Park, Won-Ho;Kim, Chae-Sil;Choi, Heon-Oh
    • Proceedings of the KSME Conference
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    • 2001.06c
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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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Optimal Design of Dynamic System Using a Genetic Algorithm(GA) (유전자 알고리듬을 이용한 동역학적 구조물의 최적설계)

  • Hwang, Sang-Moon;Seong, Hwal-Gyeong
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.1 s.94
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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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Static Compliance Analysis & Multi-Objective Optimization of Machine Tool Structures Using Genetic Algorithm(II) (유전자 알고리듬을 이용한 공작기계구조물의 정강성 해석 및 다목적 함수 최적화(II))

  • 이영우;성활경
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2001.10a
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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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Optimization of a Membrane with a Center Hole using Natural Element Method and Genetic Algorithm (자연요소법과 유전자 알고리듬을 사용한 원공 평판의 최적설계)

  • Lee, Sang-Bum;Seong, Hwal-Gyeng;Cheon, Ho-Jeong
    • Journal of the Korean Society for Precision Engineering
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    • v.25 no.2
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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.

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

  • Park, Cheol-Woo;Cheon, Ho-Jeong
    • Journal of the Korean Society for Precision Engineering
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    • v.29 no.4
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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.