• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1809-1814
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• 2003

Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1404-1409
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• 2011

The fundamental research for the mobile robot navigation using the numerical optimization method is presented. We define the mobile robot navigation problem as an unconstrained optimization problem to minimize the cost function with the pose error between the goal position and the position of a mobile robot. Using the nonlinear least squares optimization method, the optimal speeds of the left and right wheels can be found as the solution of the optimization problem. Especially, the rotational speed of wheels of a mobile robot can be directly related to the overall speed of a mobile robot using the Jacobian derived from the kinematic model. It will be very useful for applying to the mobile robot navigation. The performance was evaluated using the simulation.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.424-431
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• 2001

This paper is concerned with the unconstrained two-dimensional cutting problem of cutting small rectangles (products), each of which has its own profit and size, from a large rectangle (material) to maximize the profit-sum of products. Since this problem is used as a sub-problem to generate a cutting pattern in the algorithms for the two-dimensional cutting stock problem, most of researches for the two-dimensional cutting stock problem have been concentrated on solving this sub-problem more efficiently. This paper improves Hifi and Zissimopoulos's recursive algorithm, which is known as the most efficient exact algorithm, by applying newly proposed upper bound and searching strategy. The experimental results show that the proposed algorithm has been improved significantly in the computational amount of time as compared with the Hifi and Zissimopulos's algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.61-70
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• 2005

In this Paper, we develop an improved branch and bound algorithm for the (un)weighted unconstrained two-dimensional cutting problem. In the proposed algorithm, we improve the branching strategies of the existing exact algorithm and reduce the size of problem by removing the dominated pieces from the problem. We apply the newly Proposed definition of dominated cutting pattern and it can reduce the number of nodes that must be searched during the algorithm procedure. The efficiency of the proposed algorithm is presented through comparison with the exact algorithm known as the most efficient.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.79-84
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• 2009

In this paper, a best-first branch and bound algorithm based upon the bottom-up approach for the unweighted unconstrained two-dimensional cutting problem is proposed to find the optimal solution to the problem. The algorithm uses simple and effective methods to prevent constructing duplicated patterns and reduces the searching space by dividing the branched node set. It also uses a efficient bounding strategy to fathom the set of patterns. Computational results are compared with veil-known exact algorithms and demonstrate the efficiency of the proposed algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.12 s.255
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• pp.1581-1587
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• 2006

An optimization formulation is proposed to minimize sound pressures in a two-dimensional cavity by controlling the attachment area of viscoelastic unconstrained damping materials. For the analysis of structural- acoustic systems, a hybrid approach that uses finite elements for structures and boundary elements for cavity is adopted. Four-parameter fractional derivative model is used to accurately represent dynamic characteristics of the viscoelastic materials with respect to frequency and temperature. Optimal layouts of the unconstrained damping layer on structural wall of cavity are identified according to temperatures and the amount of damping material by using a numerical search algorithm.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.187-188
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• 2011

This paper proposes the hybrid Complex-PSO algorithm based on the complex search method and particle swarm optimization (PSO) for unconstrained optimization. This hybridization intends to produce faster and more accurate convergence to the optimum value. These hybrid will concentrate on determining the dynamic load model parameters, the ZIP model and induction motor model parameters. Measurement-based parameter estimation, which employs measurement data to derive load model parameters, is used. The theoretical foundation of the measurement-based approach is system identification. The main objective of this paper is to demonstrate how the standard particle swarm optimization and complex method can be improved through hybridization of the two methods and the results will be compared with that of their original forms.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.641-646
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• 2001

Eigenstructure assignment is a typical method with the capability of consideration of the time-domain specifications in designing a linear control system. In this paper, we propose a new method for eigenstructure to achieve desired eigenvectors more precisely than with the conventional method. In the proposed method, the conventional eigenstructure assignment problem is interpreted as a constrained optimization one, and it converted into an unconstrained optimization problem to deal with the problem easily. Numerical examples are presented to illustrate the proposed flexible eigenstructure assignment method.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.268-275
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• 2000

The increasing technological demands of today call for complex systems, which in turn involve a series of optimization problems with some equality or inequality constraints. In this paper, we presents a real-coded genetic algorithm(RCGA) as an optimization tool which is implemented by three genetic operators based on real coding representation. Through a lot of simulation works, the optimum settings of its control parameters are obtained on the basis of global off-line robustness for use in off-line applications. Two optimization problems are Presented to illustrate the usefulness of the RCGA. In case of a constrained problem, a penalty strategy is incorporated to transform the constrained problem into an unconstrained problem by penalizing infeasible solutions.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.269-279
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• 2007

This paper describes a direct search method for a class of linearly constrained optimization problem. Through research we find it can be treated as an unconstrained optimization problem. And with the decrease of dimension of the variables need to be computed in the algorithms, the implementation of convergence to KKT points will be simplified to some extent. Convergence is shown under mild conditions which allow successive frames to be rotated, translated, and scaled relative to one another.