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In this contribution a hybrid particle swarm optimization (PSO) based algorithm is applied to high school timetabling problems.
A metaheuristic based algorithm is applied to solve the model and experiments are established to validate the efficiency of the proposed method.
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After obtaining the result from three approaches rule based algorithm was applied to all three techniques to improve the accuracy.
The Newly-developed personal thermal sensation models and group-of-people-based thermal sensation models generated by support vector machine (SVM) based algorithms are applied to evaluate the occupants' thermal sensations.
A wavelet packet transform (WPT -based algorithm is applied on the vibrating response of the plates to extract WPT -basedsed dalgorithmsisive feapplied
Secondly, a random number-based algorithm is applied to uniquely compute the parameters, and its results are compared to those generated through a conventional, nonlinear optimization method using spreadsheet software.
Typically, SVM-based algorithm is applied to road detection through semi-supervised or supervised online learning [20, 21].
More specifically, when this learning-based algorithm is applied in BWA networks, we only need to translate the scheduling objectives to the system reward (or cost) and then connect the scheduling problem with the ingredients of the solution framework.
Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset.
The HSA which is a meta-heuristic based algorithm is then applied to optimize the influential geometrical parameters.
Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com