• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.1
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• pp.71-78
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• 2005

Rescaled Simulated Annealing(RSA) has been devised for improving the disadvantage of Simulated Annealing(SA) which requires tremendous amount of computation time. RSA and SA have been for optimization of truss and satellite structures and for comparison of results from two algorithms. Ten bar truss structure which has continuous design variables are optimized.. As a practical application, a satellite structure is optimized by the two algorithms. Weights of satellite upper platform and propulsion module are minimized. MSC/NASTRAN is used for the static and dynamic analysis. The optimization results of the RSA are compared with results of the classical SA. The numbers of optimization iterations could be effectively reduced by the RSA.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.63-81
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• 1995

This paper presents a model and algorithm for solving the multi-factor plant layout problem. The model can incorporate more than two factors that may be either quantitative or qualitative. The algorithm is based on simulated annealing, which has been successfully applied for the solution of combinatorial problems. A set of problems previously used by various authors is solved to demonstrate the effectiveness of the proposed methods. The results indicate that the proposed methods can yield good quality for each of eleven test problems.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.78-82
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• 2003

In this paper, simulated annealing technique was used to estimate the rock joint characteristics, RMR(rock mass rating) values, to overcome the defects of ordinary kriging. Ordinary kriging reduced the variance of data, so lost the characteristics of distribution. Simulated annealing technique could reflect the distribution feature and the spatial correlation of the original data. Through the comparisons between three times simulations, the uncertainty of the simulation could be quantified, and sufficient results were obtained.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.83-103
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• 1993

This paper addresses an optimization problem faced by a multi-server and multi-class customer system in manufacturing facilities and service industries. This paper presents a model of an integrated problem of server allocation and customer type partitioning. We approximate the problem through two types of models to make it tractable. As soution approach, the simulated annealing heuristic is constructed based on the general simulated annealing method. Computational results are presented.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.651-652
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• 2008

In this paper, we suggest a new multiobjective optimization algorithm which is based on the simulated annealing(SA) method. The proposed algorithm uses population-based simulated annealing and adapts elitism in the process of selection.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.12
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• pp.589-594
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• 2000

The fuzzy controllers have been applied to the power system stabilizer due to its excellent properties on the nonlinear systems. But the design process of fuzzy controller requires empirical and heuristic knowledge of human experts as well as many trial-and-errors in general. This process is time consuming task. This paper presents an parameters tuning method of the fuzzy power system stabilizer using the genetic algorithm and simulated annealing(SA). The proposed method searches the local minimum point using the simulated annealing algorithm. The proposed method is applied to the one-machine infinite-bus of a power system. Through the comparative simulation with conventional stabilizer and fuzzy stabilizer tuned by genetic algorithm under various operating conditions and system parameters, the robustness of fuzzy stabilizer tuned by proposed method with respect to the nonlinear power system is verified.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.139-155
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• 2006

The facility location problem of this paper is distinguished from the maximal covering location problem and the flxed-charge facility location problem. We propose the maximal profiting location problem (MPLP) that is the facility location problem maximizing profit with multi-product. We apply to the simulated annealing algorithm, the stochastic evolution algorithm and the accelerated simulated annealing algorithm to solve this problem. Through a scale-down and extension experiment, the MPLP was validated and all the three algorithm enable the near optimal solution to produce. As the computational complexity is increased, it is shown that the simulated annealing algorithm' is able to find the best solution than the other two algorithms in a relatively short computational time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.409-412
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• 2004

Today, meta-heuristic algorithms have been much attention by researcher because they have the power of solving combinational optimization problems efficiently. As the result, many variants of a meta-heuristic algorithm (e.g., simulated annealing) have been proposed for specific application domains. However, there are few efforts to classify them into a unified software framework, which is believed to provide the users with the reusability of the software, thereby significantly reducing the development time of algorithms. In this paper, we present an object-oriented framework to be used as a general tool for efficiently developing variants of simulated annealing algorithm. The interface classes in the framework achieve the modulization of the algorithm, and the users are allowed to specialize some of the classes appropriate for solving their problems. The core of the framework is Algorithm Configuration Pattern (ACP) which facilitates creating user-specific variants flexibly. Finally, we summarize our experiences and discuss future research topics.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1512-1518
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• 2004

Rescaled Simulated Annealing (RSA) has been adapted to solve combinatorial optimization problems in which the available computational resources are limited. Simulated Annealing (SA) is one of the most popular combinatorial optimization algorithms because of its convenience of use and because of the good asymptotic results of convergence to optimal solutions. However, SA is too slow to converge in many problems. RSA was introduced by extending the Metropolis procedure in SA. The extension rescales the state's energy candidate for a transition before applying the Metropolis criterion. The rescaling process accelerates convergence to the optimal solutions by reducing transitions from high energy local minima. In this paper, structural optimization examples using RSA are provided. Truss structures of which design variables are discrete or continuous are optimized with stress and displacement constraints. The optimization results by RSA are compared with the results from classical SA. The comparison shows that the numbers of optimization iterations can be effectively reduced using RSA.