• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.451-459
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• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.554-558
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• 2001

In this study, a hybrid method is used to search the pseudo-optimal solution for the I-dimentional nesting problem. This method is composed of the genetic algorithm for the global search and a simple heuristic one for the local search near the pseudo optimal solution. Several simulation results show that the hybrid method gives very satisfactory results.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.357-370
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• 2000

This study has adapted a genetic algorithm for an optimal design for the first time. the models that was used a simulation are the first and second order response surfaces model, Using an genetic algorithm in D-opimal it is more efficient than previous algorithms to get an object function. Not like other algorithm without any restrictions like troublesome about the initial solution not falling into a local optimal solution it's the most suitable algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.19-26
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• 2001

A design issue for a flexible manufacturing system is to find the number of each resource type which assures a given production ratio with a minimal cost. The FMS is modeled as a closed queueing network with a local buffer of limited capacity. An efficient method to determine this optical configuration is presented. The proposed method consists of three steps : 1) determine a lower configuration, 2) derive a heuristic solution, 3) find the optimal solution. The derived heuristic solutions are close to the optical solution.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.15-19
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• 2004

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5B
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• pp.295-303
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• 2007

An algorithm is proposed to seek a local optimal solution of the network utility maximization problem in a wireless mesh network, where the architecture being considered is an infrastructure/backbone wireless mesh network. The objective is to achieve proportional fairness amongst the end-to-end flows in wireless mesh networks. In order to establish the communication constraints of the flow rates in the network utility maximization problem, we have presented necessary and sufficient conditions for the achievability of the flow rates. Since wireless mesh networks are generally considered as a type of ad hoc networks, similarly as in wireless multi-hop network, the network utility maximization problem in wireless mesh network is a nonlinear nonconvex programming problem. Besides, the gateway/bridge functionalities in mesh routers enable the integration of wireless mesh networks with various existing wireless networks. Thus, the rate optimization problem in wireless mesh networks is more complex than in wireless multi-hop networks.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.293-298
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• 1993

In this paper, methods for computing the time-optimal motion of a robotic manipulator are presented that considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. The optimization problem can be reduced to a search for the time-optimal path in the n-dimensional position space. These paths are further optimized with a local path optimization to yield a global optimal solution. Time-optimal motion of a robot with an articulated arm is presented as an example.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.234-239
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• 1993

Most of the control problem is for the redundant manipulators use the pseudo-inverse control, thit is, the redundancy is resolved by the pseudo-inverse of the Jacobian matrix and then the controller is designed based on this resolution. However, this pseudo-inverse control has some problems when the redundant robot repeats the cyclic tasks. This is because the pseudo-inverse resolution is a local solution that generates the different configurations of the robot arm for the same hand position. Therefore it is necessary to find the global solution that maintains the optimal configuration of the robot for the repetitive tasks. In this paper, we want to propose a redundancy resolution method by the optimal theory that uses the calculus of variation. The problem formulations are : first to convert the optimal resolution problem to an optimal control problem and then to resolve the redundancy using the necessary conditions of optimal control.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.161-169
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• 2016

This paper is intended to propose a novel approach to select an optimal site for a small-scaled artificial recharge system installation using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with geospatial data. TOPSIS is a MCDM (Multi-Criteria Decision Making) method to choose the preferred one of derived alternatives by calculating the relative closeness to an ideal solution. For applying TOPSIS, in the first, the topographic shape representing optimal recovery efficiency is defined based on a hydraulic model experiment, and then an appropriate surface slope is determined for the security of a self-purification capability with DEM (Digital Elevation Model). In the second phase, the candidate areas are extracted from an alluvial map through a morphology operation, because local alluvium with a lengthy and narrow shape could be satisfied with a primary condition for the optimal site. Thirdly, a shape file over all candidate areas was generated and criteria and their values were assigned according to hydrogeologic attributes. Finally, TOPSIS algorithm was applied to a shape file to place the order preference of candidate sites.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.41-47
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• 2015

The bandwidth minimization problem (BMP) has been classified as NP-complete because the polynomial time algorithm to find the optimal solution has been unknown yet. This paper suggests polynomial time heuristic algorithm is to find the solution of bandwidth minimization problem. To find the minimum bandwidth ${\phi}^*=_{min}{\phi}(G)$, ${\phi}(G)=_{max}\{{\mid}f(v_i)-f(v_j):v_i,v_j{\in}E\}$ for given graph G=(V,E), m=|V|,n=|E|, the proposed algorithm sets the maximum degree vertex $v_i$ in graph G into global central point (GCP), and labels the median value ${\lceil}m+1/2{\rceil}$ between [1,m] range. The graph G is partitioned into subgroup, the maximum degree vertex in each subgroup is set to local central point (LCP), and we adjust the label of LCP per each subgroup as possible as minimum distance from GCP. The proposed algorithm requires O(mn) time complexity for label to all of vertices. For various twelve graph, the proposed algorithm can be obtains the same result as known optimal solution. For one graph, the proposed algorithm can be improve on known solution.