• Journal of Korean Society on Water Environment
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• v.23 no.5
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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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• 2005.06a
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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.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.305-313
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• 2002

The genetic algorithm(GA), an optimization technique based on the theory of natural selection, has proven to be relatively robust means to search for global optimum. It is converged near to the global optimum point without auxiliary information such as differentiation of function. When studying some optimization problems with continuous variables, it was found that premature saturation was reached that is no further improvement in the object function could be found over a set of iterations. Also, the general GA oscillates in the region of the new global optimum point so that the speed of convergence is decreased. This paper is to propose the concept of restarting and elitist preserving strategy as a measure to overcome this difficulty. Some benchmark examples are studied involving 3-bar truss and cantilever beam with plane stress elements. The modifications to GA improve the speed of convergence.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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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.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.153-159
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• 2009

In order to estimate release water from reservoirs located on ungaged watersheds, an algorithm was suggested based on hydrologic reservoir routing and real time calibrating watershed parameters. A prototype - simple computer program was developed to implement the algorithm with Genetic Algorithm technic. The program was applied to a mid-size reservoir and its ungauged watershed area using observed rainfall data, spillway gates operation data and reservoir water stage time series data under a existing storm event. The result shows that the algorithm and the prototype would be useful to simulate released water from reservoirs.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.1-10
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• 2006

Load redistribution algorithm is a critical factor in computer system. In a receiver-initiated load redistribution algorithm, receiver(underloaded processor) continues to send unnecessary request messages for load transfer until a sender(overloaded processor) is found while the system load is light. Therefore, it yields many problems such as low CPU utilization and system throughput because of inefficient inter-processor communications until the receiver receives an accept message from the sender in this environment. This paper presents an approach based on genetic. algorithm(GA) for dynamic load redistribution in heterogeneous distributed systems. In this scheme the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages.

• Industrial Engineering and Management Systems
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• v.3 no.1
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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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• v.11 no.4
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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.

• Proceedings of the KIEE Conference
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• 1996.07b
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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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• v.5 no.1
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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.