View source: R/DiscrimOD_assist.r
getPSOInfo | R Documentation |
Create a list with PSO parameters for optimal discrimination design search.
getPSOInfo(
nSwarm = 32,
maxIter = 100,
freeRun = 1,
tol = 1e-06,
c1 = 2.05,
c2 = 2.05,
w0 = 1.2,
w1 = 0.2,
w_var = 0.8,
vk = 4
)
nSwarm |
A integer number of swarm size in PSO algorithm. |
maxIter |
A integer number of maximal PSO iterations. |
freeRun |
A number between |
tol |
A small value for the tolerance, |
c1 |
The value of cognitive parameter in PSO updating procedure. The default is 2.05. |
c2 |
The value of social parameter in PSO updating procedure. The default is 2.05. |
w0 |
The value of starting inertia weight in PSO updating procedure. The default is 1.2. |
w1 |
The value of ending inertia weight in PSO updating procedure. The default is 0.2. |
w_var |
A number between |
vk |
The value of velocity clamping parameter. The default is 4. |
checkConv |
A logical value which controls whether PSO checks the stopping criterion during updating procedure.
Specify |
A list of PSO parameter settings.
Chen, R.-B., Chang, S.-P., Wang, W., Tung, H.-C., and Wong, W. K. (2015). Minimax optimal designs via particle swarm optimization methods. Statistics and Computing, 25(5):975-988.
# Get default settings with specified swarm size and maximal number of iterations.
PSO_INFO <- getPSOInfo(nSwarm = 32, maxIter = 100)
# If wanted to disable L-BFGS for the inner optimization loop and
# use NestedPSO algorithm (Chen et al., 2015), we need the options
# for the two-layer PSO: c(outer loop option, inner loop option)
NESTEDPSO_INFO <- getPSOInfo(nSwarm = c(16, 32), maxIter = c(100, 200))
# Also, disable the L-BFGS algorithm
LBFGS_NOTRUN <- getLBFGSInfo(IF_INNER_LBFGS = FALSE)
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