• 한국정보통신학회논문지
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• 제13권8호
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• pp.1483-1490
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• 2009

본 논문에서는 수정된 유전자 알고리즘과 퍼지 추론 시스템을 이용한 무인 자율주행 이송장치의 다중경로계획을 연구하였다. 기존의 다중경로계획을 위한 방법으로는 최적화 알고리즘들을 이용한 작업별회귀 방법과 매시간 각 개체마다 경로를 재계획하는 방법이 있다. 이러한 방법들은 한 대의 이송장치가 작업을 하기 위해서는 한 대 이상의 이송장치가 정지해야하므로 시간과 에너지 측면에서 비효율적이며, 연산량이 많아 오류가 발생할 가능성이 있다. 본 논문에서는 이러한 문제점들을 해결하기 위해 수정된 유전자 알고리즘과 퍼지 추론 시스템을 이용한 다중경로계획을 제안한다. 제안한 알고리즘의 성능 평가를 위하여 무인 자율주행이 가능한 2대의 이송장치를 설계 제작하였고 지게차와 동일한 주행 제어부를 탑재하여 다중경로계획을 실험하였다. 실험 결과, 빠르고 최적화된 경로 계획과 효율적인 충돌 회피가 가능함을 확인 할 수 있었다.

• 한국정보통신학회논문지
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• 제11권11호
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• pp.2158-2165
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적 함수(fitness function)와 가우시안 가중치가 적용된 멤버십 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 백터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing(SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• Journal of Computational Design and Engineering
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• 제2권3호
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

• Structural Engineering and Mechanics
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• 제32권5호
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• pp.667-683
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• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• 제어로봇시스템학회논문지
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• 제17권6호
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• pp.587-595
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• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

• 한국농공학회논문집
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• 제47권3호
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• 대한산업공학회지
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• 제30권4호
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• pp.346-354
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• 2004

Most industrial logistic systems have focused on carrying products from manufacturers or distribution centers to customers. In recent years, they are faced with the problem of integrating reverse flows into their transportation systems. In this paper, we address the vehicle routing problems with mixed delivery and pick-up(VRPMDP). Mixed operation of delivery and pick-up during a vehicle tour requires rearrangement of the goods on board. The VRPMDP considers the reshuffling time of goods at customers, hard time windows, and split operation of delivery and pick-up. We construct a mixed integer mathematical model and propose a new genetic algorithm named GAMP for VRPMDP. Computational experiments on various types of test problems are performed to evaluate GAMP against the modified Dethloff's algorithm. The results show that GAMP reduces the total vehicle operation time by 5.9% on average, but takes about six times longer computation time.

• 한국시뮬레이션학회논문지
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• 제6권1호
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• KIEE International Transactions on Electrophysics and Applications
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• 제4C권3호
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• pp.123-128
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• 2004

In electrical impedance tomography (EIT), the internal resistivity distribution of the unknown object is computed using the boundary voltage data induced by different current patterns using various reconstruction algorithms. This paper presents a new image reconstruction algorithm based on the genetic algorithm (GA) via a two-step approach for the solution of the EIT inverse problem, in particular for the reconstruction of "static" images. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton-Raphson algorithm at the expense of an increased computational burden.rden.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (3)
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• pp.744-746
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• 1998

외판원(Traveling Salesman Problem)는 계산 복잡도가 매우 높으므로 이를 해결하려는 다양한 방법들이 제시되어 왔다. 최근에는 특히 휴리스틱(Heuristic) 에 기반한 유전 알고리즘(Genetic Algorithm)에 위한 방법이 관심을 집중시키고 있고, 이를 위한 다양한 교잡(Crossiver)연산자와 돌연변이(Mutation) 연산자들이 발표되고 있다. 돌연변이연산자는 지역해에 빠지는 것을 방지하며, 유용한 유전 특성을 잃어버릴 위험이 있는 교잡 연산자의 단점을 보완할 수 있다. 본 논문에서는 새로운 돌연변이 연산자를 개발하여 적용한 유전 알고리즘으로 외판원 문제를 해결한다.