Description Usage Arguments Details Value Author(s) References See Also Examples
Solves global optimization problems via Biogeography-Based Optimization method.
1 | bbo(fn, lower, upper, DisplayFlag = TRUE, RandSeed, control = bbo.control(), ...)
|
fn |
the function to be optimized (minimized). |
lower, upper |
two vectors specifying scalar real lower and upper bounds on each parameter to be optimized, so that the i-th element of |
DisplayFlag |
TRUE or FALSE, whether or not to display, default is |
RandSeed |
random number seed |
control |
a list of control parameters; see |
... |
further arguments to be passed to |
Given an objective function, this method performs biogeography-based optimization and returns the minimum cost for the given objective function.
The output of the function bbo
is a list containing the following elements:
prop
, a list of control parameters for BBO for the current run:
pModify
pMutate
KEEP
popSize
maxGen
numVar
orderDep
minCost
, a list containing the following elements:
bestMember
: the best set of parameters found.
bestValue
: the value of fn
corresponding to bestMember
.
bestHabitat
a list containing the following elements:
itersBestValue
: the best value of fn
at each iteration.
itersBestMember
: the best member at each iteration.
itersAvg
: the average population cost at each iteration.
For package bbo: Sarvesh Nikumbh<snikumbh@mpi-inf.mpg.de> Maintainer: Sarvesh Nikumbh<snikumbh@mpi-inf.mpg.de>
For BBO method: Prof. D. Simon, Cleveland State University, Ohio.
D. Simon, "Biogeography-Based Optimization", IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702-713, December 2008.
bbo.control
for control arguments
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## --------------------
## Rosenbrock function:
## --------------------
## It has a global minimum f(x) = 0 at (1,1).
## Kindly note that the first parameter passed to the
## objective function should be the vector of parameters
## to be optimized.
Rosenbrock <- function(x){
x1 <- x[1]
x2 <- x[2]
return( 100 * (x2 - x1 * x1)^2 + (1 - x1)^2 )
}
bbo(Rosenbrock, -5, 5, control =
bbo.control(pMutate = 0.4,
numVar = 2,
popSize = 50,
KEEP = 5,
maxGen = 20))
|
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