Pseudo-random Search Optimisation Algorithm of Price (1977)
Fits a model to data, using the pseudo-random search algorithm of Price (1977), a random-based fitting technique.
pseudoOptim(f, p,..., lower, upper, control = list())
f |
function to be minimised, its first argument should be the vector of parameters over which minimization is to take place. It should return a scalar result, the model cost, e.g the sum of squared residuals. |
p |
initial values of the parameters to be optimised. |
... |
arguments passed to funtion |
lower |
minimal values of the parameters to be optimised; these must be specified; they cannot be -Inf. |
upper |
maximal values of the parameters to be optimised; these must be specified; they cannot be +Inf. |
control |
a list of control parameters - see details. |
The control argument is a list that can supply any of the
following components:
npop, number of elements in the population. Defaults to max(5*length(p),50).
numiter, maximal number of iterations to be performed.
Defaults to 10000. The algorithm either stops when numiter
iterations has been performed or when the remaining variation is less
than varleft.
centroid, number of elements from which to estimate a new parameter vector, defaults to 3.
varleft, relative variation remaining; if below this value the algorithm stops; defaults to 1e-8.
verbose, if TRUE, more verbose output will contain the parameters
in the final population, their respective population costs and the cost
at each succesful interation. Defaults to FALSE.
see the book of Soetaert and Herman (2009) for a description of the algorithm AND for a line to line explanation of the function code.
a list containing:
par |
the optimised parameter values. |
cost |
the model cost, or function evaluation associated to the optimised parameter values, i.e. the minimal cost. |
iterations |
the number of iterations performed. |
and if control\$verbose is TRUE:
poppar |
all parameter vectors remaining in the population, matrix of dimension (npop,length(par)). |
popcost |
model costs associated with all population parameter vectors, vector of length npop. |
rsstrace |
a 2-columned matrix with the iteration number and the model cost at each succesful iteration. |
Karline Soetaert <karline.soetaert@nioz.nl>
Soetaert, K. and Herman, P. M. J., 2009. A Practical Guide to Ecological Modelling. Using R as a Simulation Platform. Springer, 372 pp.
Price, W.L., 1977. A Controlled Random Search Procedure for Global Optimisation. The Computer Journal, 20: 367-370.
amp <- 6
period <- 5
phase <- 0.5
x <- runif(20)*13
y <- amp*sin(2*pi*x/period+phase) + rnorm(20, mean = 0, sd = 0.05)
plot(x, y, pch = 16)
cost <- function(par)
sum((par[1] * sin(2*pi*x/par[2]+par[3])-y)^2)
p1 <- optim(par = c(amplitude = 1, phase = 1, period = 1), fn = cost)
p2 <- optim(par = c(amplitude = 1, phase = 1, period = 1), fn = cost,
method = "SANN")
p3 <- pseudoOptim(p = c(amplitude = 1, phase = 1, period = 1),
lower = c(0, 1e-8, 0), upper = c(100, 2*pi, 100),
f = cost, control = c(numiter = 3000, verbose = TRUE))
curve(p1$par[1]*sin(2*pi*x/p1$par[2]+p1$par[3]), lty = 2, add = TRUE)
curve(p2$par[1]*sin(2*pi*x/p2$par[2]+p2$par[3]), lty = 3, add = TRUE)
curve(p3$par[1]*sin(2*pi*x/p3$par[2]+p3$par[3]), lty = 1, add = TRUE)
legend ("bottomright", lty = c(1, 2, 3),
c("Price", "Mathematical", "Simulated annealing"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.