• 한국물환경학회지
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• 제23권5호
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• pp.697-704
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• 2007

This study proposes an integrated technique of Genetic Algorishim (GA) and Geographic Information System (GIS) for designing the water quality monitoring networks. To develop solution scheme of the integrated system, fitness functions are defined by the linear combination of five criteria which stand for the operation objectives of water quality monitoring stations. The criteria include representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness level is obtained through calculations of the fitness functions and input data from GIS. To find the most appropriate parameters for the problems, the sensitivity analysis is performed for four parameters such as number of generations, population sizes, probability of crossover, and probability of mutation. Using the parameters resulted from the sensitivity analysis, the developed system proposed 110 water quality monitoring stations in the Nakdong River. This study demonstrates that the integrated technique of GA and GIS can be utilized as a decision supporting tool in optimized design for a water quality monitoring network.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1080-1085
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• 2005

A hybrid control scheme is proposed for the stabilization of backward movement along simple paths for a vehicle composed of a truck and six trailers. The hybrid comprises the combination of a linear quadratic regulator (LQR) and a neurocontroller (NC) that is trained by a genetic algorithm (GA). Acting singly, either the NC or the LQR are unable to perform satisfactorily over the entire range of the operation required, but the proposed hybrid is shown to be capable of providing good overall system performance. The evaluation function of the NC in the hybrid design has been modified from the conventional type to incorporate both the squared errors and the running steps errors. The reverse movement of the trailer-truck system can be modeled as an unstable nonlinear system, with the control problem focusing on the steering angle. Achieving good backward movement is difficult because of the restraints of physical angular limitations. Due to these constraints the system is impossible to globally stabilize with standard smooth control techniques, since some initial states necessarily lead to jack-knife locks. This paper demonstrates that a hybrid of neural networks and LQR can be used effectively for the control of nonlinear dynamical systems. Results from simulated trials are reported.

• 한국전산구조공학회논문집
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• 제15권2호
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• pp.305-313
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• 2002

유전자 알고리즘은 적자 생존과 자연친화의 유전이론을 기초로 하여 이루어진 탐색기법이다. 유전자 알고리즘은 미분 정보 등과 같은 부가적인 정보없이 수렴함으로 전역적 최적값을 탐색하는 강인한 탐색기법으로 알려져 있다. 유전자 알고리즘은 연속형의 설계변수를 가지는 문제에서 세대가 계속 진행되어도 목적함수의 개선이 없이 조기에 수렴하는 경우가 있다. 또한 전역적 최적값 근처에서 수렴하지 못하고 목적함수값이 진동하여 수렴속도가 떨어지는 단점이 있다. 본 연구에서는 위와 같은 유전자 알고리즘의 단점을 보완하고자 재시동 조건과 엘리트 보존방법을 제안하였다. 수정된 유전자 알고리즘의 유용성을 검증하기 위해 3부재 트러스와 평면응력 외팔보에 적용하여 수렴 속도의 향상을 확인하였다.

• 대한산업공학회지
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• 제34권3호
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• pp.362-372
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• 2008

Manufacturers, or vendors, and their customers continue to adopt vendor-managed inventory(VMI) program to improve supply chain performance through collaboration achieved by consolidating replenishment responsibility upstream with vendors. In this paper, we construct a mixed integer linear programming model and propose a genetic algorithm for the integrated inventory and routing problems with lost sales maximizing the total profit in the VMI supply chains which comprise of a single manufacturer and multi-retailer. The proposed GA is compared with the mathematical model on the various sized test problems with respect to the solution quality and computation time. As a result, the GA demonstrates the capability of reaching solutions that are very close to those obtained by the mathematical model for small problems and stay within 3.2% from those obtained by the mathematical model for larger problems, with a much shorter computation time. Finally, we investigate the effects of the cost and operation variables on the total profit of the problem as well as the GA performance through the sensitivity analyses.

• 한국방재학회 논문집
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• 제9권6호
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• pp.153-159
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• 2009

본 연구에서 하천 홍수위 예측을 위해 미계측 유역내에 위한 저수지에서의 하천 방류를 추정하는 수문학적 저수지 추적 기법을 저수위 관측데이터와 여수로 게이트 개방 데이터를 토대로 저수지 방류량을 추정하는 알고리즘을 수립하고, 이를 반영한 유전자 알고리즘 기반의 프로토타입을 개발하여 유역매개변수를 실시간으로 자동보정할 수 있도록 하였다. 대상 유역과 저수지에서 기왕의 호우발생시의 관측 데이터를 적용하여 프로토타입에 적용성을 검토한 결과, 유의성이 있는 것으로 나타났다.

• 한국컴퓨터정보학회논문지
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• 제11권1호
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• pp.1-10
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• 2006

부하재분배 방법은 컴퓨터시스템에서 중요한 요소이다. 수신자 개시 부하재분배 알고리즘에서는 전체시스템이 저부하일 때 수신자(저부하 프로세서)가 부하를 이전받기 위해 송신자(과부하 프로세서)를 발견할 때까지 불필요한 이전 요청 메시지를 계속 보내게 된다. 따라서 이같은 상황에서는 과부하 상태인 송신자 프로세서로부터 승인 메시지를 받기까지 불필요한 프로세서간 통신으로 인하여 프로세서의 이용률이 저하되고, 타스크 처리율이 낮아지는 문제점이 발생한다. 본 논문에서는 이질형 분산 시스템에서의 동적 부하재분배를 위해 유전 알고리즘을 기반으로 하는 접근 방법을 제안한다. 이 기법에서는 불필요한 요청메시지를 줄이기 위해 요청 메시지가 전송될 프로세서들이 제안된 유전 알고리즘에 의해 결정된다.

• Industrial Engineering and Management Systems
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• 제3권1호
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• pp.78-84
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• 2004

To optimize movement of a multi-joint robot arm is known to be a difficult problem, because it is a kind of redundant system. Although the end-effector is set its position by each angle of the joints, the angle of each joint cannot be uniquely determined by the position of the end-effector. There exist the infinite number of different sets of joint angles which represent the same position of the end-effector. This paper describes how to manage the angle of each joint to move its end-effector preferably on an X-Y plane with obstacles in the end-effector’s reachable area, and how to optimize the movement of a multi-joint robot arm, evading obstacles. The definition of “preferable” movement depends upon a purpose of robot operation. First, we divide viewpoints of preference into two, 1) the standpoint of the end-effector, and 2) the standpoint of joints. Then, we define multiple objective functions, and formulate it into a multi-objective programming problem. Finally, we solve it using multi-purpose genetic algorithm, and obtain reasonable results. The method described here is possible to add appropriate objective function if necessary for the purpose.

• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.862-864
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• 1996

This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic algorithm(GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transferring problem to minimize over load generated load point outage in each sub-section. This kind of the approach is one of the most difficult procedure which becomes a combination problems. A new approach using GA Was developed for this problem. We proposed a tree search algorithm which satisfied the tree constraint. GA is general purpose optimization techniques based on principles inspired from the biological evolution such as natural selection, genetic recombination and survival of the fittest Test results for the model system with 24 nodes and 29 branches are reported in the paper.

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.