• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.93-101
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• 2004

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. The performance of proposed algorithm shows better than that of the conventional algorithm through various experiments.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.151-160
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• 2004

Genetic algorithm is the theory of grafting the principle of survival of the fittest in genetics on to the computer algorithm and it is used to solve the optimization problems, especially the shape and size optimization of the structure in Architectural problems. In the size optimization problem discrete variables are used, but series variables have to be used in the shape optimization problem because of the incongruenty. The purpose of this study is to obtain the optimum shape of cable domes by using the real coding genetic algorithm. Generally, the structural performance of the cable domes is influenced very sensitively by pre-stress, geometry and length of the mast because of its flexible characteristic. So, it is very important to decide the optimum shape to get maximum stiffness of cable domes. We use the model to verify the usefulness of this algorithm for shape optimization and analyze the roof system of Seoul Olympic Gymnastic Arena as analytical model of a practical structures. It is confirmed lastly that the optimum shape domes have more stiffness than initial shape ones.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.9-12
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• 2001

An active control of the vibration transmitted by longitudinal load in flight control system is investigated numerically. The flight control system is modeled as a finite, thin shell cylinder with constant thickness. A vibration source is generated by exterior monopole source. Distributed piezoelectric actuator is used to control of the vibration. Thin shell theory is used to formulate the numerical models. The amplitude of vibration at discrete location and power transmission are minimized by analytical optimization method. Genetic algorithm is used as numerical optimization method to search optimal actuator position and size which amplitude of vibration is minimized.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.461-464
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• 2007

In this paper, we propose a fuzzy Kalman filtering to deal with a estimation error covariance. The T-S fuzzy model structure is further rearranged to give a set of linear model using standard Kalman filter theory. And then, to minimize the estimation error covariance, which is inferred using the fuzzy system. It can be used to find the exact Kalman gain. We utilize the genetic algorithm for optimizing fuzzy system. The proposed state estimator is demonstrated on a truck-trailer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Journal of electromagnetic engineering and science
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• v.3 no.1
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• pp.17-21
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• 2003

In this paper, we design an optimal planar array geometry for maximum side-lobe reduction. The concept of thinned array is applied to obtain an optimal two dimensional(2-D) planar array structure. First, a 2-D rectangular array with uniform spacing is used as an initial planar array structure. Next, we modify the initial planar array geometry with the aid of thinned array theory in order to reduce the maximum side-lobe level. This is implemented by a genetic algorithm under some constraint, minimizing the maximum side-lobe level of the 2-D planar array. It is shown that the optimized planar array structure can achieve low side-lobe level without optimizing the excitations of the array antennas.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1950-1959
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• 2001

An active control of the vibration transmitted by longitudinal load in flight control system is investigated numerically. The flight control system is modeled as a finite, thin shell cylinder with constant thickness. A vibration source is generated by exterior monopole source. Distributed piezoelectric actuator is used to control of the vibration. Thin shell theory is used to formulate the numerical models. The amplitude of vibration at discrete location and power transmission are minimized by analytical optimization method. Genetic algorithm is used as numerical optimization method to search optimal actuator position and size which amplitude of vibration is minimized.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.46-55
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• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.166-169
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• 1997

For parallel processing, the compiler partitions a loaded program into a set of tasks and makes a schedule for the tasks that will minimize parallel processing time for the loaded program. Building an optimal schedule for a given set of partitioned tasks of a program has known to be NP-complete. In this paper we introduce a GA(Genetic Algorithm)-based scheduling method in which a chromosome consists of two parts of a string which decide the number and order of tasks on each processor. An additional computation is used for feasibility constraint in the chromosome. By granularity theory, a partitioned program is categorized into coarse-grain or fine-grain types. There exist good heuristic algorithms for coarse-grain type partitioning. We suggested another GA adaptive to the coarse-grain type partitioning. The infeasibility of chromosome is overcome by the encoding and operators. The number of processors are decided while the GA find the minimum parallel processing time.

• Journal of computational fluids engineering
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• v.17 no.4
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• pp.56-68
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• 2012

Nowadays, Unmanned Combat Air Vehicle(UCAV) has become an important aircraft system for the national defense. For its efficiency and survivability, shape optimization of UCAV is an essential part of its design process. In this paper, shape optimization of UCAV was processed for aerodynamic performance improvement and Radar Cross Section(RCS) reduction using Multi Objective Genetic Algorithm(MOGA). Lift and induced drag, friction drag, RCS were calculated using panel method, boundary layer theory, Physical Optics(PO) approximation respectively. In particular, calculation applied Radar Absorbing Material(RAM) was performed for the additional RCS reduction. Results are indicated that shape optimization is performed well for improving aerodynamic performance, reducing RCS. Further study will be performed with higher fidelity tools and consider other design segments including structure.