Simulated Annealing using an Adaptive Search Vector

Miki, Mitsunori; Hiwa, Satoru; Hiroyasu, Tomoyuki

doi:10.1109/iccis.2006.252256

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Simulated Annealing Algorithm is a simple and effective metaheuristic optimization algorithm for solving optimization problems in large search domains. In fact, this Algorithm applies to issues that finding an approximate response for general optimization, is more important than finding a precise response to local optimum at a limited and specific time [5] . The gradual Annealing technique is used by metallurgists to achieve a state in which the solid is well-developed and its energy is minimized.…”

Section: Description Of the Simulated Annealing (Sa)mentioning

confidence: 99%

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Designing a metaheuristic mining algorithm to separate two-color points in a two-dimensional environment

2019

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The separation of color points is one of the important issues in computational geometry, which is used in various parts of science; it can be used in facility locating, image processing and clustering. Among these, one of the most widely used computational geometry in the real-world is the problem of covering and separating points with rectangles. In this paper, we intend to consider the problemof separating the two-color points sets, using three rectangles. In fact, our goal is to separate desired blue points from undesired red points by three rectangles, in such a way that these three rectangles contain the most desire points. For this purpose, we provide a metaheuristic algorithm based on the simulated annealing method that could separates blue points from input points, , in time order O (n) with the help of three rectangles. The algorithm is executed with C# and also it has been compared and evaluated with the optimum algorithm results. The results show that our recommended algorithm responses is so close to optimal responses, and also in some cases we obtains the exact optimal response.

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Section: Description Of the Simulated Annealing (Sa)mentioning

confidence: 99%

“…In 2009, the problem of covering the points with two Isothetic rectangles was defined by Saha and Dos [5] . A set of n points in the page is covered by two Isothetic rectangles at any arbitrarily orientation.…”

Section: Research Backgroundmentioning

confidence: 99%

Designing a metaheuristic mining algorithm to separate two-color points in a two-dimensional environment

2019

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“…It is inspired from thermodynamics to simulate the physical process of annealing of molten metals [26,27]. It obtains the minimum value of energy by simulating annealing which is a process employed to obtain an impeccable crystal by gradual cooling of molten metals in order to keep the system of melt in a thermodynamic equilibrium at a given temperature [28]. Thus, it exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure.…”

Section: Simulated Annealingmentioning

confidence: 99%

Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing

Joseph

Muthukumaran

2015

Int J Adv Manuf Technol

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Weld quality is a very important working aspect of the manufacturing and construction industries. In this research work, an attempt has been made to optimize the parameters of activated tungsten inert gas (A-TIG) welding of sintered hotforged AISI 4135 steel produced through the powder metallurgy route. Experiments were performed based on Taguchi L9 orthogonal array. Response surface methodology was used to create regression equations, and process parameters were optimized using genetic algorithm (GA) and simulated annealing (SA). Process parameter optimization is multi-input to single output (tensile strength), in which the quality of output depends upon input parameters like current, voltage, welding speed, and gas flow rate. The present study was conducted to maximize the output of the A-TIG welding of sintered hot-forged AISI 4135 steel and fix the input parameters. The results indicate that the voltage and current have a maximum influence on tensile strength in A-TIG-welded joint. Confirmation experiments have also been conducted to validate the optimized parameters.

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“…It is inspired from thermodynamic to simulate the physical process of annealing of molten metals [27,28]. It obtains the minimum value of energy by simulating annealing which is a process employed to obtain a perfect crystal by gradual cooling of molten metals in order to keep the system of melt in a thermodynamic equilibrium at given temperature [29]. Thus, it exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure.…”

Section: Simulated Annealingmentioning

confidence: 99%

Comparative Study of Algorithms for Response Surface Optimization

Yeniay

2014

MCA

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Response Surface Methodology (RSM) is a method that uses a combination of statistical techniques and experimental design for modelling and optimization problems. Many researchers have studied the integration of heuristic methods and RSM in recent years. The purpose of this study is to compare two popular heuristic methods, namely Genetic Algorithms (GA) and Simulated Annealing (SA), with two commonly used gradient-based methods, namely Sequential Quadratic Programming (SQP) and Generalized Reduced Gradient (GRG), to obtain optimal conditions. Moreoever, real quadratic and cubic response surface models are selected from literature and used in this study. The comparison results indicate that the heuristic methods outperform the traditional methods on majority of the problems.

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Simulated Annealing using an Adaptive Search Vector

Cited by 8 publications

References 7 publications

Designing a metaheuristic mining algorithm to separate two-color points in a two-dimensional environment

Designing a metaheuristic mining algorithm to separate two-color points in a two-dimensional environment

Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing

Comparative Study of Algorithms for Response Surface Optimization

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