• Journal of Electrical Engineering and Technology
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• 제3권4호
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• pp.451-459
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• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 추계학술대회 논문집
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• pp.567-568
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• 2007

• Journal of Computational Design and Engineering
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• 제3권4호
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• 대한기계학회논문집A
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• 제25권4호
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• pp.671-684
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• 2001

Several design problems of composite structures are studied via a global optimizer based on attraction regions. MSC/NASTRAN is adopted for static and eigenvalue analysis. The method of modified feasible direction in DOT is used for local optimization. Through the review of global optimization algorithms, the triangular patch algorithm is selected because the algorithm is known to be efficient, robust and powerful for general nonlinear optimization problems. For general applicability, various mechanical properties are considered as design objectives; strain energy, eigenvalue, weight, displacement, and buckling load. In all cases considered, the triangular patch algorithm results in a lot of optimum points and useful design patterns, that are not easy by local algorithms or conventional global algorithms can be determined.

• 국제초고층학회논문집
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• 제8권4호
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• pp.291-302
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• 2019

We performed calculations combining optimization technologies and Computational Fluid Dynamics (CFD) aimed at reducing wind forces and mitigating wind environments (local strong winds) around buildings. However, the Reynolds Averaged Navier-stokes Simulation (RANS), which seems somewhat inaccurate, needs to be used to create a realistic CFD optimization tool. Therefore, in this study we explored the possibilities of optimizing calculations using RANS. We were able to demonstrate that building configurations advantageous to wind forces could be predicted even with RANS. We also demonstrated that building layouts was more effective than building configurations in mitigating local strong winds around tall buildings. Additionally, we used the Convolutional Neural Network (CNN) as an airflow prediction method alternative to CFD in order to increase the speed of optimization calculations, and validated its prediction accuracy.

• 정보과학회 논문지
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• 제41권12호
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• pp.1145-1151
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• 2014

본 논문에서는 전술 백본망에서 부하 분산을 위한 다중 경로 지역 최적화 기법을 제안하였다. 제안된 기법은 라우팅 메트릭을 전역 메트릭과 지역 메트릭으로 구분하여 관리한다. 전역 메트릭은 라우팅 프로토콜을 통해 다른 라우터들에게 전파되며 루프 방지가 보장되는 다중 경로 구성에 사용되고, 지역 메트릭은 링크 사용율을 반영하여 링크 과부하 발생시 우회 경로를 탐색하는 용도로 활용되며 각 라우터 내에서만 관리된다. 모의 실험을 통해 다중 경로 지역 최적화 기법 적용시 사용자 트래픽이 효과적으로 가용 링크들을 통해 분산되는 것을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.44-46
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• 2008

This paper Proposes a new couple particle swarm optimization (CPSO) for multimodal functions. In this method, main particles are generated uniformly using Faure-sequences, and move accordingly to cognition only model. If any main particle detects the movement direction which has local optimum, this particle would create a new particle beside itself and make a couple. After that, all couples move accordingly to conventional particle swarm optimization (PSO) model. If these couples tend toward the same local optimum, only the best couple would be kept and the others would be eliminated. We had applied this method to some analytic multimodal functions and successfully locate all local optima.

• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1061-1069
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• 2000

In this paper, a new genetic algorithm is proposed for solving multimodal function optimization problems that are not easily solved by conventional genetic algorithm(GA)s. This algorithm finds one of local optima first and another optima at the next iteration. By repeating this process, we can locate all the local solutions instead of one local solution as in conventional GAs. To avoid converging to the same optimum again, we devise a new genetic operator, called a Mendel operator which simulates the Mendels genetic law. The proposed algorithm remembers the optima obtained so far, compels individuals to move away from them, and finds a new optimum.

• 로봇학회논문지
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• 제9권4호
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• pp.197-205
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• 2014

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

• 대한산업공학회지
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• 제37권4호
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• pp.408-414
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• 2011

The objective of this paper is to define local optimization algorithms (LOA) to solve Resource-Constrained Project Scheduling Problem (RCPSP) and analyze the performance of these algorithms. By representing solutions with activity list, three primitive LOAs, i.e. forward and backward improvement-based, exchange-based, and relocation-based LOAs are defined. Also, combined LOAs integrating two primitive LOAs are developed. From the experiments with standard test set J120 generated using ProGen, the FBI-based LOA demonstrates to be an efficient algorithm. Moreover, algorithms combined with FBI-based LOA and other LOA generate good solutions in general. Among the considered algorithms, the combined algorithm of FBI-based and exchangebased shows best performance in terms of solution quality and computation time.