xegaPopulation (version 1.0.0.11)

ExponentialAdditiveCooling: 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)

Value

The temperature at time k.

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).

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)