Computational Experience with Generalized Simulated Annealing Over Continuous Variables

Brooks, Daniel G.; Verdini, William A.

doi:10.1080/01966324.1988.10737248

Cited by 38 publications

(23 citation statements)

References 10 publications

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“…The convergence ratio to the global minimum was 64:2%. When compared to previous implementations [15][16][17], the proposed SA algorithm with crystallization factor showed an improved convergence ratio. …”

Section: Proposed Problem Instancesmentioning

confidence: 96%

“…In [17], the same next candidate distribution as in [16] is employed, but it is shown that the selection of ∆r is a critical choice. An appropriate choice of this parameter is strictly dependent on the objective function F (x), and that bad choices may lead to a deterioration of the performance of the algorithm.…”

Section: Simulated Annealingmentioning

confidence: 99%

“…An appropriate choice of this parameter is strictly dependent on the objective function F (x), and that bad choices may lead to a deterioration of the performance of the algorithm. In [17] it is suggested to choose appropriate values by presampling the objective function. It is suggested to split the algorithm in two phases: a global phase in which the algorithm globally explores the feasible regions and a local phase which starts from the best point observed in the global phase (which, hopefully, is close enough to the global optimum) and employs the same SA algorithm but with a much lower value for the step size ∆r, which reduces the algorithm to a local exploration of the region around the best point observed in the global phase.…”

Section: Simulated Annealingmentioning

confidence: 99%

“…The directions in [16,17] are randomly sampled from the uniform distribution over the unit (n − 1) dimensional sphere, and the step size if the same in each direction. In this way the feasible region is explored in an isotropic way and it is assumed that the objective function behaves in the same way in every direction.…”

Section: Simulated Annealingmentioning

confidence: 99%

See 3 more Smart Citations

Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

Martins¹,

Tsuzuki²

2009

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. This work deals with the problem of minimizing the waste of space that occurs on a rotational placement of a set of irregular bi-dimensional items inside a bi-dimensional container. This problem is approached with a heuristic based on Simulated Annealing (SA) with adaptive neighborhood. The objective function is evaluated in a constructive approach, where the items are placed sequentially. The placement is governed by three different types of parameters: sequence of placement, the rotation angle and the translation. The rotation applied and the translation of the polygon are cyclic continuous parameters, and the sequence of placement defines a combinatorial problem. This way, it is necessary to control cyclic continuous and discrete parameters. The approaches described in the literature deal with only type of parameter (sequence of placement or translation). In the proposed SA algorithm, the sensibility of each continuous parameter is evaluated at each iteration increasing the number of accepted solutions. The sensibility of each parameter is associated to its probability distribution in the definition of the next candidate.

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Section: Proposed Problem Instancesmentioning

confidence: 96%

Section: Simulated Annealingmentioning

confidence: 99%

Section: Simulated Annealingmentioning

confidence: 99%

Section: Simulated Annealingmentioning

confidence: 99%

See 2 more Smart Citations

Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

Martins¹,

Tsuzuki²

2009

Bulletin of the Polish Academy of Sciences: Technical Sciences

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show abstract

“…Brooks & Verdini [2] showed that the selection of Δr is a critical choice. They observed that an appropriate choice of this parameter is strictly dependent on the objective function F(x), and the appropriate value can be determined by presampling the objective function.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neighborhood Heuristics for Simulated Annealing over Continuous Variables

Martins¹,

Sato²,

Tsuzuki³

2012

Simulated Annealing - Advances, Applications and Hybridizations

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Generalized simulated annealing for calibration sample selection from an existing set and orthogonalization of undesigned experiments

Kalivas

1991

Journal of Chemometrics

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Generalized simulated annealing (GSA) is an optimization procedure for locating the global optimum (maximum or minimum) of multidimenisonal continuous functions. GSA has been modified for optimization of discrete functions. Selection of calibration samples from an existing set defines discrete optimization and GSA is used to select optimal sets of calibration samples for specific analysis samples. The procedure is applied to near‐infrared spectra. When compared to using the complete set of 37 calibration samples, concentration prediction errors were reduced 50%–100% by using select sets of two to seven calibration samples. Additionally, GSA was able to improve a poorly designed experiment. GSA devised augmented experimental designs such that the overall experimental design (original plus augmented) was more orthogonal than the original.

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Computational Experience with Generalized Simulated Annealing Over Continuous Variables

Cited by 38 publications

References 10 publications

Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

Adaptive Neighborhood Heuristics for Simulated Annealing over Continuous Variables

Generalized simulated annealing for calibration sample selection from an existing set and orthogonalization of undesigned experiments

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