• Title, Summary, Keyword: 진화적 구조 최적화

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Optimum Shape Design of a Rotating-Shaft Using ESO Method ESO 법을 이용한 회전축의 형상최적화

  • Yang, Bo-Suk;Kim, Yong-Han
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.360-364
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    • 2001
  • 본 논문에서는 최근의 진화적 구조최적화(ESO) 전략을 회전축의 형상최적화에 적용하였으며, 각 계산 스텝마다 단위 유한요소의 크기를 변경함으로써 기존의 방법보다 빠르고 정확한 최적형상에 수렴하는 새로운 방법을 제시하였다. 축요소의 직경을 시스템 설계변수로 하였으며, 축중량의 감소, 공진배율(Q-factor)의 감소 및 충분한 위험속도의 분리여유를 갖도록 목적함수를 설정하였다. 불평형응답 및 굽힙응력의 구속조건을 부가하였으며, 목적함수에 대한 설계변수의 감도해석을 수행하였다. 전동기축계에 대한 적용 결과로부터 주파수와 동적 구속조건하의 로터베어링 시스템에 대한 축 형상 최적화에 ESO법이 효과적으로 이용될 수 있음을 확인하였다.

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Comparative Study on Element Removal Methods for ESO (진화적 구조 최적화를 위한 요소 제거법의 비교 연구)

  • 한석영
    • Journal of The Korean Society of Manufacturing Technology Engineers
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    • v.9 no.5
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    • pp.112-118
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    • 2000
  • In case ESO(evolutionary structural optimization) which is one of topology optimization methods, the element removal ratio is fixed throughout topology optimization by 1 or 2%. As a result it has no flexibility for various types of structures and thus the rate of convergence might not be efficient. Thus various element removal methods were developed in order to improve the efficiency of ESO. In this paper, various element removal methods for ESO are compared with each other for a bracket and a short cantilever. In addition, a new improved bi-directional element removal method is suggested in order to obtain much better optimized topology. From the comparative results of the examples, it is verified that all of the developed various element removal methods are very effective, and the suggested element removal method is the most effective.

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Development of a Material Mixing Method using ESO (진화적 구조 최적화를 이용한 재료 혼합법의 개발)

  • 한석영;이수경;신민석
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • pp.259-264
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    • 2003
  • 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.

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인공 진화에 의한 학습 및 최적화

  • 장병탁
    • ICROS
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    • v.1 no.3
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    • pp.52-61
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    • 1995
  • 본 고에서는 진화계산의 동작 원리와 이론적 기반에 대해 살펴봄으로써 그 원리를 이해하고 앞으로의 응용가능성에 대하여 고찰하고자 한다. 이를 위해 먼저 대부분의 진화 알고리즘에 공통되는 기본 구성 요소와 계산절차를 기술하고, 진화 알고리즘을 이용하여 특정문제를 풀고자 할 때 고려할 사항에 대하여 기술한다. 다음에는 간단한 응용 문제를 예로 들어 이 문제에 진화 알고리즘을 적용하고 그 동작과정을 추적함으로써 실제 적용에 있어서의 여러 가지 결정사항과 그 수행과정을 구체적으로 살펴본다. 또한 진화 알고리즘의 이론적 배경을 이해하기 위해 스키마와 빌딩 블록 그리고 스키마 정리에 대해서 알아본다. 마지막으로 진화계산방식과 다른 지능적 계산 기술들과의 융합 가능성의 예로서, 유전 프로그래밍에 의한 신경망 구조의 설계 및 학습에 대하여 살펴본다.

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Reliability-Based Topology Optimization Based on Bidirectional Evolutionary Structural Optimization (양방향 진화적 구조최적화를 이용한 신뢰성기반 위상최적화)

  • Yu, Jin-Shik;Kim, Sang-Rak;Park, Jae-Yong;Han, Seog-Young
    • Journal of The Korean Society of Manufacturing Technology Engineers
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    • v.19 no.4
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    • pp.529-538
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    • 2010
  • This paper presents a reliability-based topology optimization (RBTO) based on bidirectional evolutionary structural optimization (BESO). In design of a structure, uncertain conditions such as material property, operational load and dimensional variation should be considered. Deterministic topology optimization (DTO) is performed without considering the uncertainties related to the design variables. However, the RBTO can consider the uncertainty variables because it can deal with the probabilistic constraints. The reliability index approach (RIA) and the performance measure approach (PMA) are adopted to evaluate the probabilistic constraints in this study. In order to apply the BESO to the RBTO, sensitivity number for each element is defined as the change in the reliability index of the structure due to removal of each element. Smoothing scheme is also used to eliminate checkerboard patterns in topology optimization. The limit state indicates the margin of safety between the resistance (constraints) and the load of structures. The limit State function expresses to evaluate reliability index from finite element analysis. Numerical examples are presented to compare each optimal topology obtained from RBTO and DTO each other. It is verified that the RBTO based on BESO can be effectively performed from the results.

A Study on Improving System Plan for the Raising Efficiency of Forest Fire Extinguishing (산림화재 진화의 효율화를 위한 제도개선 방안에 관한 연구)

  • Choi, Kyu-Chool;Youn, Soon-Man
    • Proceedings of the Korea Institute of Fire Science and Engineering Conference
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    • pp.252-261
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    • 2008
  • 우리나라에서는 산림화재가 발생하면 산림청이 주관하여 지방자치단체와 산림청 지방기관의 직원들을 동원하여 통합진압대를 구성하여 산불진화활동에 나선다. 이 때 산림청은 산불진화에 있어 통합지휘권을 갖고 산불현장에 출동한 지자체 소속 공무원이나 산불감시요원 및 각종 진압요원, 출동한 소방공무원 등을 현장 지휘하는 구조로 운영되고 있다. 경기도 양평군에서 최근 5년 동안 발생했던 산불을 추적하여 산불의 발생에서 진압까지전 과정을 분석하였다. 일선에서 산불이 발생하면 주민들은 일상적으로 소방관서에 신고하고 있으며, 신고를 접한 소방관서는 즉각 출동하여 진화하고 마지막 잔불정리까지를 소방관서가 담당하고 있다. 소방관서에는 산불진화를 위한 예산지원이나 인력지원은 전무한 상태의 구조로 되어 있는 현재의 산불진화체계는 여러 문제점으로 인하여 산불의 효율적인 진화가 어렵고, 산불 발생 시 산불확대로 피해를 키우고 있다는 지적을 받고 있다. 이러한 산불진화체계의 제도적인 문제점을 파악하고 개선하여 산불진화의 최적화를 위한 개선책을 제시한다.

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Optimization of Multi-objective Function based on The Game Theory and Co-Evolutionary Algorithm (게임 이론과 공진화 알고리즘에 기반한 다목적 함수의 최적화)

  • Sim, Kwee-Bo;Kim, Ji-Yoon;Lee, Dong-Wook
    • Journal of Korean Institute of Intelligent Systems
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    • v.12 no.6
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    • pp.491-496
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    • 2002
  • Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.

Shape & Topology Optimum Design of Truss Structures Using Genetic Algorithms (유전자 알고리즘에 의한 트러스의 형상 및 위상최적실계)

  • Park, Choon Wook;Youh, Baeg Yuh;Kang, Moon Myung
    • Journal of Korean Society of Steel Construction
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    • v.13 no.6
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    • pp.673-681
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    • 2001
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

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Direction Vector for Efficient Structural Optimization with Genetic Algorithm (효율적 구조최적화를 위한 유전자 알고리즘의 방향벡터)

  • Lee, Hong-Woo
    • Journal of the Korean Association for Spatial Structures
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    • v.8 no.3
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    • pp.75-82
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    • 2008
  • In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

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Structural Optimization of Planar Truss using Quantum-inspired Evolution Algorithm (양자기반 진화알고리즘을 이용한 평면 트러스의 구조최적화)

  • Shon, Su-Deok;Lee, Seung-Jae
    • 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.