Packaged: 2021-08-17 16:32:20 UTC; ville.satopaa

Packaged: 2022-04-25 09:44:27 UTC; ville.satopaa

Title: Metaheuristic Optimization Framework for Preprocessing
Description: Automation of preprocessing often requires computationally costly
preprocessing combinations. This package helps to find near-best combinations
faster. Sub heuristics supported are random and grid restarts, taboo list,
decreasing probability for accepting inferior solutions and location of
previously best solution candidate is compared to. The package is intended
to be used with package 'preprocomb' and takes its 'GridClass' object as input.
Imports: utils, reshape2, ggplot2, preprocomb

faster. Metaheuristics supported are taboo search, simulated annealing, reheating
and late acceptance. Start conditions include random and grid starts. End conditions
include all iteration rounds completed, objective threshold reached and convergence.
Metaheuristics, start and end conditions can be hybridized and hyperparameters optimized.
Parallel computations are supported. The package is intended to be used with package
'preprocomb' and takes its 'GridClass' object as input.
Imports: utils, methods, stats, reshape2, ggplot2, preprocomb (>=
Suggests: testthat, knitr, rmarkdown, rpart

person("Lala", "Septem Riza", email = "lala.s.riza@upi.edu", role = c("aut","cre")),
person("Iip", email = "iip@student.upi.edu", role = "aut"),
person("Eddy", "Prasetyo Nugroho", email = "eddypn@upi.edu", role = "aut"))
Author: Lala Septem Riza [aut, cre], Iip [aut], Eddy Prasetyo Nugroho [aut]
Description: An implementation of metaheuristic algorithms for continuous
optimization. Currently, the package contains the implementations of the following algorithms: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>) and whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>).