• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 B
• /
• pp.507-509
• /
• 1999

In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.

• 한국지능시스템학회논문지
• /
• 제2권3호
• /
• pp.58-67
• /
• 1992

This paper presents a method on applying Genetric Algorithms(GA), which is a well-know high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilized Sugeno's hybrid inference method. which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the iptimal parameters in the FLC. The proposed approach will be demonstrated using 2 d. o. f robot manipulator to verify its effectiveness.

• 지능정보연구
• /
• 제19권4호
• /
• pp.55-79
• /
• 2013

본 연구에서는 집중형 센터를 가진 역물류네트워크(Reverse logistics network with centralized centers : RLNCC)를 효율적을 해결하기 위한 혼합형 유전알고리즘(Hybrid genetic algorithm : HGA) 접근법을 제안한다. 제안된 HGA에서는 유전알고리즘(Genetic algorithm : GA)이 주요한 알고리즘으로 사용되며, GA 실행을 위해 0 혹은 1의 값을 가질 수 있는 새로운 비트스트링 표현구조(Bit-string representation scheme), Gen and Chang(1997)이 제안한 확장샘플링공간에서의 우수해 선택전략(Elitist strategy in enlarged sampling space) 2점 교차변이 연산자(Two-point crossover operator), 랜덤 돌연변이 연산자(Random mutation operator)가 사용된다. 또한 HGA에서는 혼합형 개념 적용을 위해 Michalewicz(1994)가 제안한 반복적언덕오르기법(Iterative hill climbing method : IHCM)이 사용된다. IHCM은 지역적 탐색기법(Local search technique) 중의 하나로서 GA탐색과정에 의해 수렴된 탐색공간에 대해 정밀하게 탐색을 실시한다. RLNCC는 역물류 네트워크에서 수집센터(Collection center), 재제조센터(Remanufacturing center), 재분배센터(Redistribution center), 2차 시장(Secondary market)으로 구성되며, 이들 각 센터 및 2차 시장들 중에서 하나의 센터 및 2차 시장만 개설되는 형태를 가지고 있다. 이러한 형태의 RLNCC는 혼합정수계획법(Mixed integer programming : MIP)모델로 표현되며, MIP 모델은 수송비용, 고정비용, 제품처리비용의 총합을 최소화하는 목적함수를 가지고 있다. 수송비용은 각 센터와 2차 시장 간에 제품수송에서 발생하는 비용을 의미하며, 고정비용은 각 센터 및 2차 시장의 개설여부에 따라 결정된다. 예를 들어 만일 세 개의 수집센터(수집센터 1, 2, 3의 개설비용이 각각 10.5, 12.1, 8.9)가 고려되고, 이 중에서 수집센터 1이 개설되고, 나머지 수집센터 2, 3은 개설되지 않을 경우, 전체고정비용은 10.5가 된다. 제품처리비용은 고객으로부터 회수된 제품을 각 센터 및 2차 시장에서 처리할 경우에 발생되는 비용을 의미한다. 수치실험에서는 본 연구에서 제안된 HGA접근법과 Yun(2013)의 연구에서 제안한 GA접근법이 다양한 수행도 평가 척도에 의해 서로 비교, 분석된다. Yun(2013)이 제안한 GA는 HGA에서 사용되는 IHCM과 같은 지역적탐색기법을 가지지 않는 접근법이다. 이들 두 접근법에서 동일한 조건의 실험을 위해 총세대수 : 10,000, 집단의 크기 : 20, 교차변이 확률 : 0.5, 돌연변이 확률 : 0.1, IHCM을 위한 탐색범위 : 2.0이 사용되며, 탐색의 랜덤성을 제거하기 위해 총 20번의 반복실행이 이루어 졌다. 사례로 제시된 두 가지 형태의 RLNCC에 대해 GA와 HGA가 각각 실행되었으며, 그 실험결과는 본 연구에서 제안된 HGA가 기존의 접근법인 GA보다 더 우수하다는 것이 증명되었다. 다만 본 연구에서는 비교적 규모가 작은 RLNCC만을 고려하였기에 추후 연구에서는 보다 규모가 큰 RLNCC에 대해 비교분석이 이루어 져야 할 것이다.

• International Journal of Control, Automation, and Systems
• /
• 제4권5호
• /
• pp.624-636
• /
• 2006

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

• Industrial Engineering and Management Systems
• /
• 제8권1호
• /
• pp.14-21
• /
• 2009

We formulate a mathematical model of remanufacturing system as multi-stage reverse Logistics Network Problem (mrLNP) with minimizing the total costs for reverse logistics shipping cost and inventory holding cost at disassembly centers and processing centers over finite planning horizons. For solving this problem, in the 1st and the 2nd stages, we propose a Genetic Algorithm (GA) with priority-based encoding method combined with a new crossover operator called as Weight Mapping Crossover (WMX). A heuristic approach is applied in the 3rd stage where parts are transported from some processing centers to one manufacturer. Computer simulations show the effectiveness and efficiency of our approach. In numerical experiments, the results of the proposed method are better than pnGA (Prufer number-based GA).

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.183-186
• /
• 2003

Hybrid genetic algorithms (HGAs) have been studied as various ways. These HGAs usually use both the global search property of genetic algorithm (GA) and the local search one of local search techniques. One of the general types, when constructing HGAs, is to incorporate a local search technique into GA loop, and then the local search technique is repeated as many iteration number as the loop. This paper proposes a new HGA with a conditional local search technique (c-HGA) that does not be repeated as many iteration number as GA loop. For effectiveness of the proposed c-HGA, a conventional HGA and GA are also suggested, and then these algorithms are compared with each other in numerical examples,

• 한국정보처리학회논문지
• /
• 제7권2호
• /
• pp.552-557
• /
• 2000

본 논문에서는 신경망의 초기 파라미터(가중치, 바이어스) 값을 최적화 시키는 GA-BP(Genetic Algorithm-Backpropagation Network) 혼합 알고리즘을 이용하여 얼굴을 인식하는 방법을 제안하였다. 입력 영상의 각 픽셀들을 신경망의 입력으로 사용하고 고정 소수점 실수값으로 이루어진 신경망의 초기 파리미터 값은 유전자 알고리즘의 개체로 사용하기 위해 비트 스트링으로 변환한다. 신경망의 오차가 최소가 되는 값을 적합도로 정의한 뒤 새롭게 정의된 적응적 재학습 연산자를 이용하여 이를 평가해 최적의 진환된 신경망을 구성한 뒤 얼굴을 인식하는 실험을 하였다. 실험 결과 학습 수렴 속도의 비교에서는 오류 역전과 알고리즘 단독으로 실행한 수렴 속도보다 제안된 알고리즘의 수렴 속도가 향상된 결과를 보였고 인식률에서 오류 역전과 알고리즘 단독으로 실행한 방법보다 2.9% 향상된 것으로 나타났다.

• Geomechanics and Engineering
• /
• 제22권1호
• /
• pp.81-95
• /
• 2020

Measurement of the unconfined compressive strength (UCS) of the rock is critical to assess the quality of the rock mass ahead of a tunnel face. In this study, extensive field studies have been conducted along 3,885 m of the new Nagasaki tunnel in Japan. To predict UCS, a hybrid model of artificial neural network (ANN) based on genetic algorithm (GA) optimization was developed. A total of 1350 datasets, including six parameters of the Measurement-While- Drilling data and the UCS were considered as input and output parameters respectively. The multiple linear regression (MLR) and the ANN were employed to develop contrast models. The results reveal that the developed GA-ANN hybrid model can predict UCS with higher performance than the ANN and MLR models. This study is of great significance for accurately and effectively evaluating the quality of rock masses in tunnel engineering.

• 대한안전경영과학회지
• /
• 제22권4호
• /
• pp.65-74
• /
• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• 대한산업공학회지
• /
• 제27권1호
• /
• pp.54-60
• /
• 2001

In this paper, a shape-based block layout (SBL) approach is presented to solve the facility layout problem with unequal-area and fixed shapes. The SBL approach employs hybrid genetic algorithm (Hybrid-GA) to find a good solution and the concept of bay structure is used. In the typical facility layout problem with unequal area and fixed shapes, the given geometric constraints of unequal-area and fixed shapes are mostly approximated to original shape by aspect ratio. Thus, the layout results require extensive manual revision to create practical layouts and it produces irregular building shapes and too much unusable spaces. Experimental results show that a SBL model is able to produce better solution and to create more practical layouts than those of existing approaches.