• Title, Summary, Keyword: Simulated Annealing

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Fast Simulated Annealing Algorithm (Simulated Annealing의 수렴속도 개선에 관한 연구)

  • 정철곤;김중규
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.27 no.3A
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    • pp.284-289
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    • 2002
  • In this paper, we propose the fast simulated annealing algorithm to decrease convergence rate in image segmentation using MRF. Simulated annealing algorithm has a good performance in noisy image or texture image, But there is a problem to have a long convergence rate. To fad a solution to this problem, we have labeled each pixel adaptively according to its intensity before simulated annealing. Then, we show the superiority of proposed method through experimental results.

Optimization of Satellite Structures by Simulated Annealing (시뮬레이티드 어닐링에 의한 인공위성 구조체 최적화)

  • Im Jongbin;Ji Sang-Hyun;Park Jungsun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.2
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    • pp.262-269
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    • 2005
  • Optimization of a satellite structure under severe space launching environments is performed considering various design constraints. Simulate annealing, one of combinatorial optimization techniques, is used to optimize the satellite. The optimization results by the simulated annealing are compared to those by the method of modified feasible direction and genetic algorithm. Ten bar truss structure is optimized for feasibility study of the simulated annealing. Finally, the satellite structure is optimized by the simulated annealing algorithm under space environment. Weights of the satellite upper platform and propulsion module are minimized with consideration of several static and dynamic constraints. MSC/NASTRAN is used to find the static and dynamic responses. Simulated annealing has been programmed and integrated with the finite element analysis program for optimization. It is shown that the simulated annealing algorithm can be extended to the optimization of space structures.

Optimization Using Gnetic Algorithms and Simulated Annealing (유전자 기법과 시뮬레이티드 어닐링을 이용한 최적화)

  • Park, Jung-Sun;Ryu, Mi-Ran
    • Proceedings of the KSME Conference
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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.

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Study on the L(2,1)-labeling problem based on simulated annealing algorithm (Simulated Annealing 알고리즘에 기반한 L(2,1)-labeling 문제 연구)

  • Han, Keun-Hee;Lee, Yong-Jin
    • Journal of Korean Institute of Intelligent Systems
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    • v.21 no.1
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    • pp.138-144
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    • 2011
  • L(2, 1)-labeling problem of a graph G = (V, E) is a problem to find an efficient way to distribute radio frequencies to various wireless equipments in wireless networks. In this work, we suggest a Simulated Annealing algorithm that can be applied to the L(2, 1)-labeling problem. By applying the suggested algorithm to various graphs we will try to show the efficiency of our algorithm.

Improving Efficiency of Minimum Dominating Set Problem using Simulated Annealing Algorithms (Simulated Annealing 알고리즘을 이용한 최소 Dominating Set 문제의 효율성 증가에 대한 연구)

  • Jeong, Tae-Eui
    • The KIPS Transactions:PartA
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    • v.18A no.2
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    • pp.69-74
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    • 2011
  • The minimum dominating set problem of a graph G is to find a smallest possible dominating set. The minimum dominating set problem is a well-known NP-complete problem such that it cannot be solved in polynomial time. Heuristic or approximation algorithm, however, will perform well in certain area of application. In this paper, we suggest three different simulated annealing algorithms and experimentally show better efficiency improvement by applying these algorithms to the graph instances developed by DIMACS.

Development of a Modified Random Signal-based Learning using Simulated Annealing

  • Han, Chang-Wook;Lee, Yeunghak
    • Journal of Multimedia Information System
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    • v.2 no.1
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    • pp.179-186
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    • 2015
  • This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.

Structural Optimization By Adaptive Simulated Annealing's Cooling Schedule Change (어댑티브 시뮬레이티드 어넬링의 냉각스케줄에 따른 구조최적설계)

  • Jung, Suk-Hoon;Park, Jung-Sun
    • Proceedings of the KSME Conference
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    • pp.1436-1441
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    • 2003
  • Recently, simulated annealing algorithms have widely been applied to many structural optimization problems. In this paper, simulated annealing, boltzmann annealing, fast annealing and adaptive simulated annealing are applied to optimization of truss structures for improvement quality of objective function and number of function evaluation. These algorithms are classified by cooling schedule. The authors have changed parameters of ASA's cooling schedule and the influence of cooling schedule parameters on structural optimization obtained is discussed. In addition, cooling schedule of BA and ASA mixed is applied to 10 bar-truss structure.

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Edge Detection Using Simulated Annealing Algorithm (Simulated Annealing 알고리즘을 이용한 에지추출)

  • Park, J.S.;Kim, S.G.
    • Journal of the Korea Society For Power System Engineering
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    • v.2 no.3
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    • pp.60-67
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    • 1998
  • Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.

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Inversion of Geophysical Data via Simulated Annealing (아닐링법에 의한 지구물리자료의 역산)

  • Kim, Hee Joon
    • Economic and Environmental Geology
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    • v.28 no.3
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    • pp.305-309
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    • 1995
  • There is a deep and useful connection between thermodynamics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and combinational or continuous optimization (finding the minimum of a given multiparameter function). At the heart of the method of simulated annealing is an analogy with the way that liquids freeze and crystallize, or metals cool and anneal. This paper provides a detailed description of simulated annealing. Although computationaly intensive, when it is carefully implemented, simulated annealing is found to give superior results to more traditional methods of nonlinear optimization.

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A Study on Simulated Annealing Algorithm in Flowshop Scheduling (Flowshop 일정계획을 위한 Simulated Annealing 알고리듬 이용)

  • 우훈식;임동순;김철한
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.21 no.45
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    • pp.25-32
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    • 1998
  • A modified simulated annealing algorithm is proposed and applied to the permutation flowshop scheduling with the makespan objective. Based on the job deletion and insertion method, a newly defined Max-min perturbation scheme is proposed to obtain a better candidate solution in the simulated annealing process. The simulation experiments are conducted to evaluate the effectiveness of the proposed algorithm against the existing heuristics and results are reported.

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