• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Structural Engineering and Mechanics
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• 제52권6호
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• pp.1099-1120
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• 2014

In this paper, reliability-based design optimization (RBDO) of structures is addressed. For this purpose, the global search and optimization capabilities of genetic algorithm (GA) are combined with the efficiency and reasonable accuracy of an advanced moment-based finite element reliability method. For performing RBDO, three variants of GA including a real-coded, a binary-coded and an improved binary-coded GA are developed. In these methods, GA performs (finite element) reliability analyses to evaluate reliability constraints. For truss structures which include finite element modeling, reliability constraints are evaluated using finite element reliability analysis. Response sensitivity required for finite element reliability analysis is obtained by direct differentiation method (DDM) rather than finite difference method (FDM). The proposed methods are examined within four standard test examples and real-world design problems. The results illustrate the superiority and efficiency of the improved binary-coded GA. Results also illustrate that DDM significantly reduces the computational cost and improves the efficiency of the optimization procedure.

• Structural Engineering and Mechanics
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• 제75권4호
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• pp.445-454
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• 2020

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.

• Steel and Composite Structures
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• 제27권3호
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• pp.297-310
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• 2018

A simple and efficient numerical optimization approach for the lightweight optimal design of composite laminated beams is presented in this paper. The proposed procedure is a combination between the finite element method (FEM) and a global optimization algorithm developed recently, namely Jaya. In the present procedure, the advantages of FEM and Jaya are exploited, where FEM is used to analyze the behavior of beam, and Jaya is modified and applied to solve formed optimization problems. In the optimization problems, the objective aims to minimize the overall weight of beam; and fiber volume fractions, thicknesses and fiber orientation angles of layers are selected as design variables. The constraints include the restriction on the first fundamental frequency and the boundaries of design variables. Several numerical examples with different design scenarios are executed. The influence of the design variable types and the boundary conditions of beam on the optimal results is investigated. Moreover, the performance of Jaya is compared with that of the well-known methods, viz. differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The obtained results reveal that the proposed approach is efficient and provides better solutions than those acquired by the compared methods.

• Smart Structures and Systems
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• 제14권1호
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• pp.55-70
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• 2014

The paper presents a multi-objective optimization strategy for a multi-type sensor placement for Structural Health Monitoring (SHM) of long span bridges. The problem is formulated for simultaneous placement of strain sensors and accelerometers (heterogeneous network) based on application demands for SHM system. Modal Identification (MI) and Accurate Mode Shape Expansion (AMSE) were chosen as the application demands for SHM. The optimization problem is solved through the use of integer Genetic Algorithm (GA) to maximize a common metric to ensure adequate MI and AMSE. The performance of the joint optimization problem solved by GA is compared with other established methods for homogenous sensor placement. The results indicate that the use of a multi-type sensor system can improve the quality of SHM. It has also been demonstrated that use of GA improves the overall quality of the sensor placement compared to other methods for optimization of sensor placement.

• 한국전산구조공학회논문집
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• 제15권3호
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• pp.453-462
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• 2002

건설공사의 설계와 시공에서 표준화된 이산형 강재단면을 이용하고 있으나, 대부분의 최적화기법에서는 표준강재단면을 사용하기 위해 별도의 이산화 과정을 가지게 되므로 설계결과의 최적성을 보장할 수 없다. 따라서, 본 논문에서는 제안된 알고리즘의 효율성을 높이기 위해 전체구조계와 구조요소계로 나누는 다단계 알고리즘을 적용하였다 수치해석 과정의 효율성과 최적해의 정확성을 예제를 통하여 비교·검토하였다.

• 대한조선학회논문집
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• 제34권1호
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• pp.93-101
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• 1997

본 연구에서는 전통적인 비선형 최적화 기법의 문제점을 극복하기 위하여 유전자알고리즘과 지식베이스의 통합을 통한 새로운 개념의 최적화 기법을 개발하였다. 여기에서는 제한조건이 있는 비선형 최적화 문제를 해결하기 위해 사용되는 전통적인 순차적 선형화 방법과 새로운 유전자 알고리즘의 장단점을 서로 보완한 하이브리드형 최적화 기법을 개발하였다. 여기에 지식베이스를 통한 최적화 지원 기법 및 최적화 모델의 자동생성 모듈을 개발하여 최적화 모텔의 성능을 한층 개선할 수 있었다. 개발된 최적화 기법의 검증을 위하여 수학적 비선형 모델을 이용한 여러가지 기법의 비교 검토를 수행하였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.329-334
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• 2006

Rail clamps are mechanical components installed to fix the container crane to its bottoms from wind blast or slip. Rail clamps should be designed to survive the harsh wind loading condition. In this study, the jaw structure that is one part of wedge-typed rail clamp is optimized, considering strength under the severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw belongs to shape optimization. In the conventional structural optimization methods, they have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using kriging interpolation method is introduced, replacing true response by approximate one. This research presents the shape optimization of a jaw using iterative kriging interpolation models and simulated annealing algorithm. The new kriging models are iteratively constructed by refining the former kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.247-254
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• 2008

The primary objective of this research is to develop an efficient design and optimization methodology for SRM Wagon Wheel Grain and to develop of software for practical designing and optimization of Wagon Wheel grains. This work will provide a design process reference guide for engineers in the field of Solid Rocket Propulsion. Using these proposed design methods, SRM Wagon Wheel grains can be designed for various geometries, their optimal solutions can be found and best possible configuration be attained thereby ensuring finest design in least possible iterations & time. The main focus is to improve computational efficiency at various levels of the design work. These have been achieved by the following way. a. Evaluation of system requirements and design objectives. b. Development of Geometric Model of Wagon Wheel grain configuration. c. Internal ballistic performance predictions. d. Preliminary designing of the Wagon Wheel grain configuration involving various independent geometric variables. e. Optimization of the grain configuration using Sequential Quadratic Programming f. In depth analysis of the optimal results considering affects of various geometric variables on ballistic parameters and analysis of performance prediction outputs have been performed g. Development of software for design and optimization of Wagon Wheel Grain. By using these proposed design methods, SRM Wagon Wheel grains can be designed by using geometric model, their optimal solutions can be found and best possible configuration be attained thereby ensuring finest design.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.208-215
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• 2021

Cosmic-ray muon scattering tomography (MST) technology is a new radiation imaging technology with unique advantages. As the performance of its image reconstruction algorithm has a crucial influence on the imaging quality, researches on this algorithm are of great significance to the development and application of this technology. In this paper, a fast inspection algorithm based on clustering analysis for the identification of the existence of nuclear materials is studied and optimized. Firstly, the principles of MST technology and a binned clustering algorithm were introduced, and then several simulation experiments were carried out using Geant4 toolkit to test the effects of exposure time, algorithm parameter, the size and structure of object on the performance of the algorithm. Based on these, we proposed two optimization methods for the clustering algorithm: the optimization of vertical distance coefficient and the displacement of sub-volumes. Finally, several sets of experiments were designed to validate the optimization effect, and the results showed that these two optimization methods could significantly enhance the distinguishing ability of the algorithm for different materials, help to obtain more details in practical applications, and was therefore of great importance to the development and application of the MST technology.