| ExponentialAdditiveCooling {xegaPopulation} | R Documentation |
Exponential additive cooling.
Description
This schedule decreases in proportion to the
inverse of exp raised to the
power of the temperature cycle in
lF$Generations() (= number of generations) fractions
between the starting temperature temp0
and the final temperature tempN.
Usage
ExponentialAdditiveCooling(k, lF)
Arguments
k |
Number of steps to discount. |
lF |
Local configuration. |
Details
Temperature is updated at the end of each generation
in the main loop of the genetic algorithm.
lF$Temp0() is the starting temperature.
lF$TempN() is the final temperature.
lF$Generations() is the number of generations (time).
Value
The temperature at time k.
References
The-Crankshaft Publishing (2023) A Comparison of Cooling Schedules for Simulated Annealing. <https://what-when-how.com/artificial-intelligence/a-comparison-of-cooling-schedules-for-simulated-annealing-artificial-intelligence/>
See Also
Other Cooling:
ExponentialMultiplicativeCooling(),
LogarithmicMultiplicativeCooling(),
PowerAdditiveCooling(),
PowerMultiplicativeCooling(),
TrigonometricAdditiveCooling()
Examples
parm<-function(x){function() {return(x)}}
lF<-list(Temp0=parm(100), TempN=parm(10), Generations=parm(50))
ExponentialAdditiveCooling(0, lF)
ExponentialAdditiveCooling(2, lF)