Bicriteria Scheduling Problem on the Two-Machine Flowshop Using Simulated Annealing

Mesgarpour, Mohammad; Kırkavak, Nureddin; Özaktaş, Haldun M.

doi:10.1007/978-3-642-12139-5_15

Cited by 1 publication

(1 citation statement)

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“…These recommendations are not absolute, however, and different proposals can be found in literature, for example close to 1.0 [25], around 0.8 [26] or even only 0.5 [27]. Often in practical applications of SA algorithms, the temperature value is fixed during numerical experiments [28]. An alternative is to choose a temperature according to a calculational criterion which has the goal of obtaining the (1) value on the basis of a set of pilot iterations, consisting of generating the neighbor solution (1) so that the assumed (1) value is ensured.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing

Kulczycki

Łukasik

2014

Studies in Computational Intelligence

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Abstract.A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, with the requirement that it has the least possible influence on the respective locations of sample elements. For this purpose an original version of the heuristic Parallel Fast Simulated Annealing method was used. In addition, those elements which change the location significantly as a result of the transformation, may be eliminated or assigned smaller weights for further analysis. As well as reducing the sample size, this also improves the quality of the applied methodology of knowledge extraction. Experimental research confirmed the usefulness of the procedure worked out in a broad range of problems of exploratory data analysis such as clustering, classification, identification of outliers and others.

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