Automation of preprocessing often requires computationally costly
preprocessing combinations. This package helps to find near-best combinations
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.
utils, methods, stats, reshape2, ggplot2, preprocomb (>=
0.3.0), doParallel, foreach

c(
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"))
Lala Septem Riza [aut, cre], Iip [aut], Eddy Prasetyo Nugroho [aut]
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>).

c(
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"),
person("Muhammad Bima", "Adi Prabowo", email = "bimaadi419@student.upi.edu", role = "aut"),
person("Enjun", "Junaeti", email="enjun@upi.edu", role = "aut"),
person("Ade Gafar", "Abdullah", email="ade_gaffar@upi.edu", role="aut"))
Lala Septem Riza [aut, cre],
Iip [aut],
Eddy Prasetyo Nugroho [aut],
Muhammad Bima Adi Prabowo [aut],
Enjun Junaeti [aut],
Ade Gafar Abdullah [aut]
An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: 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>),  whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).