• Journal of Korean Institute of Industrial Engineers
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• v.21 no.1
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• pp.33-49
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• 1995

Simulated annealing(SA) has been admitted as a general purpose optimization technique which can be utilized for almost all kinds of combinatorial optimization problems without much difficulty. But there are still some weak points to be resolved, one of which is the slow speed of convergence. In this study, we carefully review various previous efforts to improve SA and propose some variations of SA which can enhance the speed of convergence to the optimum solution. Then, we apply the revised SA algorithms to the scheduling and hardware allocation problems occurring in high-level synthesis(HLS) of VLSI design. We confirm the efficiency of the proposed methods through several HLS examples.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.09a
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• pp.156-160
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• 2002

실험적 베리오그램의 모델링에 SA(Simulated Annealing)기법을 이용하였다. 최소 자승법의 해를 구하기 위하여 기존의 상용 프로그램에서 많이 이용되고 있는 반복법에 근거한 방법에 비해서 SA 기법은 초기 가정값에 크게 영향을 받지 않고 일정한 모델 인자의 값을 제시하였다. 임의의 초기 가정값을 입력하여도 충분한 반복 계산을 통하여 목적함수의 값이 광역적 최소값으로 수렴하는 것을 확인할 수 있었다. 베리오그램 모델이 일반적으로 비선형 모델이기 때문에 목적함수의 지역적 최소값으로의 수렴이 문제가 되고 이로 인하여 구해지는 인자의 값이 정확하지 않을 수 있지만 SA 기법을 이용하여 최소 자승법의 해를 구하게 되면 정확한 인자의 값을 구할 수 있음을 확인하였다.

• Journal of the Optical Society of Korea
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• v.13 no.1
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• pp.92-97
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• 2009

In this paper, a conventional simulated annealing (SA) method for optimization of a kinoform computer generated hologram (CGH) is analyzed and the SA method is modified to reduce a reconstruction error rate (ER) of the CGH. The dependences of the quantization level of the hologram pattern and the size of the data on the ER are analyzed. To overcome saturation of the ER, the conventional SA method is modified as it magnifies a Fourier-transformed pattern in the intermediate step. The proposed method can achieve a small ER less than 1%, which is impossible in the conventional SA method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.939-944
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• 2001

Genetic algorithm is modelled on natural evolution and simulated annealing is based on the simulation of thermal annealing. Both genetic algorithm and simulated annealing are stochastic method. So they can find global optimum values. For compare efficiency of SA and GA's, some function value was maximized. In the result, that was a little better than GA's.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.458-465
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• 2000

In the field of structural optimization simulated annealing (SA) algorithm has widely been adopted as an optimizer with the positive features of SA such as simplicity of the algorithm and possibility of finding global solution However, annealing process of SA algorithm based on random generator with the zeroth order structural information requires a large of number of iterations highly depending on cooling schedules and stopping criteria. In this paper, MSA algorithm is presented in the form of two phase annealing process with the effective cooling schedule and stopping criteria. With the application to optimal seismic design of steel structures, the performance of the proposed MSA algorithm has been demonstrated with respect to stability and global convergence of the algorithm

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.425-428
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• 2000

In this paper, we propose a new method based on SA(simulated annealing) with a fast algorithm for 3D image reconstructrion from the coded apereture images. The reconstructed images can be significantly improved by SA and to large computation cost of SA can be significantly reduced by the fast algorithm.

• IE interfaces
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• v.18 no.4
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• pp.370-381
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• 2005

This study is concerned with seeking the optimal allocation(disposition) for maximizing utility of consolidating old fashioned and new air defense weapon system like SAM-X(Patriot missile) and developing efficient solution algorithm based on simulated annealing(SA) algorithm. The SED(selection by effectiveness degree) procedure is implemented with an enhanced SA algorithm in which neighboring solutions could be generated only within the optimal feasible region by using a specially designed PERTURB function. Computational results conducted on the problem sets with a variety of size and parameters shows the significant efficiency of our SED algorithm over existing methods in terms of both the computation time and the solution quality.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.288-289
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• 1992

The method of simulated annealing is a technique that has recently attracted significant attention as suitable for optimization problem of very large scale. If the temperature is too high, then some of the structure created by the heuristic will be destroyed and unnecessary extra work will be done. If it is too low then solution is lost, similar to the case of a quenching cooling schedule in the Simulated Annealing (SA) phase. Therefore, a crucial issue in this study is the determination of the starting temperature and cooling schedule for SA phase.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.8
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• pp.775-780
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• 2004

This paper describes the realization of the Ka-band group-delay equalized filter desisted with the help of a new hybrid method of Simulated Annealing(SA) and Conjugate Gradient(CG), to be employed by the multi-channel Input Multiplexer for a satellite use, each channel of which comprises a channel filter and a group-delay equalizer. The SA and CG find circuit parameters of an 8th order elliptic function filter and a 2-pole equalizer, respectively. Measurement results demonstrate that the performances of the designed component meet the specifications, and validate the design methods.