I tried optim using the SANN algoithm. To start things out I tried the example
of solving the "traveling salesman" problem as given in the
documentation. The example works just fine. But if I comment out the line:
set.seed(123) # chosen to get a good soln relatively quickly
More often than not it doesn't converge to the optimum solution as shown in
the example. Alos with trace on it seems that the algoritm is easily fooled by a
local mimimum as once it gets close to the solution it seems to get
"stuck" and repeatedly returns the same value: A sample run:
sann objective function values
initial value 29625.000000
iter 5000 value 13972.000000
iter 10000 value 13501.000000
iter 15000 value 13501.000000
iter 20000 value 13501.000000
iter 25000 value 13487.000000
iter 29999 value 13487.000000
final value 13487.000000
sann stopped after 29999 iterations
Not that familiiar with the algoritmn Is that just a drawback of the algorithm
or can I adjust the anealling temperature (temp) or the maximum
"temperature" (tmax) or even the mximum number of iterations to kick
it out of what appears to be a local minimum? I am willing to sacrifice extra
compute time for better accuracy.
Kevin
