• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.999-1004
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• 2004

A numerical method and algorithms is proposed to perform optimization of non-linear response structures. An analytical and numerical method based finite element method is also proposed for the transformation of non-linear response into linear response. Loads transformed from this method are defined as the equivalent linear loads. With the loads and the transformed response, linear static optimization is performed for nonlinear response structure with geometric and/or material non-linearity. The results of the optimization are compared with them of typical non-linear response optimization using finite difference method. The proposed method is very efficient and derives good solution.

• Steel and Composite Structures
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• 제6권5호
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• pp.435-457
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• 2006

The paper presents the cost optimization of composite floor trusses composed from a reinforced concrete slab of constant depth and steel trusses consisting of hot rolled channel sections. The optimization was performed by the nonlinear programming approach, NLP. Accordingly, a NLP optimization model for composite floor trusses was developed. An accurate objective function of the manufacturing material, power and labour costs was proposed to be defined for the optimization. Alongside the costs, the objective function also considers the fabrication times, and the electrical power and material consumption. Composite trusses were optimized according to Eurocode 4 for the conditions of both the ultimate and the serviceability limit states. A numerical example of the optimization of the composite truss system presented at the end of the paper demonstrates the applicability of the proposed approach.

• 대한토목학회논문집
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• 제5권3호
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• pp.49-59
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• 1985

탄성(彈性) 이론(理論)에 의하여 트러스의 형상최적화(形狀最適化) 문제(問題)를 형성(形成)하게 되면 부재(部材)의 단면적(斷面積)과 절점(節點)의 좌표(座標)를 동시에 고려(考慮)해야 하는 복잡(複雜)한 비선형(非線型) 계획문제(計劃問題)가 된다. 이런 비선형(非線形) 계획문제(計劃問題)를 해석(解析)할 수 있도록 제시(提示)된 기법(技法)이 별로 없고 현재 사용(使用)하고 있는 기법(技法)들도 실제(實際)의 적용(適用)에 제한(制限)을 받는 경우가 많다. 그러므로 트러스의 형태(形態), 재하조건(載荷條件) 등에 구애됨이 없이 트러스의 형상(形狀)을 최적화(最適化)할 수 있는 일반(一般) 해석기법(解析技法)이 필요(必要)한 것이다. 이에 본연구(本硏究)에서는 전(全) 해석과정(解析過程) two-phases로 나누어 phase 1 에서는 단면(斷面)을 최적화(最適化)하고 phase 2 에서는 트러스의 절점좌표(節點座標)를 변수(變數)로 하여 형상(形狀)을 최적화(最適化)하는 알고리즘을 개발(開發)한 것이다. 이 알고리즘의 phase 1 에서 유도(誘導)된 비선형(非線型) 계획문제(計劃問題)를 SUMT 문제(問題)로 변환(變換)시켜 Modified Newton-Raphson Method에 의한 SUMT 법(法)을 채택(採擇)하고 phase 2 에서는 Rosenbrock Method의 일방향(一方向) 탐사기법(探査技法)에 의해 목적함수(目的凾數)만이 최소(最小)가 되도록 하는 기법(技法)을 도입(導入)하여 최적화(最適化) 알고리즘 개발(開發)하였다. 개발(開發)된 알고리즘을 트러스의 형태(形態), 설계제약조건(設計制約條件), 재하조건(載河條件) 등을 변화(變化)시켜 가면서 수종(數種)의 트러스에 적용(適用)하여 수치계산(數値計算)을 실시(實施)하고 그 결과(結果)를 다른 알고리즘의 결과(結果)와 정교(正較)하므로서 개발(開發)된 알고리즘의 타당성(妥當性) 안정성(安定性) 적용성(適用性)을 검토(檢討)하였다. 연구(硏究) 결과(結果) 개발(開發)된 이 two-phases 알고리즘은 트러스의 설계조건(設計條件)에 구애받지 않고 트러스의 형상최적화(形狀最適化)에 적용(適用)할 수 있으며 안정성(安定性)있게 빠른 속도(速度)로 최적해(最適解)에 수렴(收斂)한다는 사실(事實)이 확인(確認)되었다. 이에 본(本) 알고리즘을 트러스의 형상최적화(形狀最適化) 알고리즘으로 새로이 제안(提案)하고 본(本) 알고리즘이트러스의 경제적(經劑的)인 설계(設計)에 도움을 줄 수 있을 것으로 사료(思料)된다.

• Structural Engineering and Mechanics
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• 제62권3호
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• pp.259-272
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• 2017

In this study, recently developed swarm intelligence algorithm called Social Spider Optimization (SSO) approach and its enhanced version of SSO algorithm with spider jump techniques is used to develop a structural optimization technique for steel space structures. The improved version of SSO uses adaptive randomness probability in generating new solutions. The objective function of the design optimization problem is taken as the weight of a steel space structure. Constraints' functions are implemented from American Institute of Steel Construction-Load Resistance factor design (AISC-LRFD) and Ad Hoc Committee report and practice which cover strength, serviceability and geometric requirements. Three steel space structures are optimized using both standard SSO and SSO with spider jump (SSO_SJ) algorithms and the results are compared with those available in the literature in order to investigate the performance of the proposed algorithms.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.9-14
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• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• 한국공간구조학회:학술대회논문집
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• 한국공간구조학회 2005년도 춘계학술발표회 및 정기총회 2권1호(통권2호)
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• pp.246-253
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algorithm are searching methods for optimum values. The object of this reserch is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic one, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm

• Advances in Computational Design
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• 제1권2호
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• pp.175-194
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• 2016

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. Dolphin Echolocation Optimization (DEO) is an optimization method based on dolphin's approach for hunting food and exploration of environment. DEO has already been developed for discrete optimization search space and here it is extended to continuous search space. DEO has simple rules and is adjustable for predetermined computational cost. DEO provides the optimum results and leads to alternative optimality curves suitable for the problem. This algorithm has a few parameters and it is applicable to a wide range of problems like other metaheuristic algorithms. In the present work, the efficiency of this approach is demonstrated using standard benchmark problems.

• 대한조선학회논문집
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• 제46권4호
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• pp.409-416
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• 2009

The structural design problems are acknowledged to be commonly multi-criteria in nature. The various multi-criteria optimization methods are reviewed and the most efficient and easy-to-use Pareto optimal solution methods are applied to structural optimization of a truss and a beam. The result of the study shows that Pareto optimal solution methods can easily be applied to structural optimization with multiple objectives, and the designer can have a choice from those Pareto optimal solutions to meet an appropriate design environment.

• Structural Engineering and Mechanics
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• 제70권6호
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• pp.649-659
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• 2019

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

• 한국공작기계학회논문집
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• 제12권3호
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• pp.82-88
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• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.