PowerMultiplicativeCooling: Power multiplicative cooling.

In xegaPopulation: Genetic Population Level Functions

Power multiplicative cooling.

Description

This schedule decreases by the inverse proportion of a power of k. lF$Alpha() should be larger than 1.

Usage

PowerMultiplicativeCooling(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. For lF$CoolingPower()==1 and lF$CoolingPower()==2 this results in the the linear and quadratic multiplicative cooling schemes in A Comparison of Cooling Schedules for Simulated Annealing. lF$Temp0() is the starting temperature. lF$Alpha() is a scaling factor. lF$CoolingPower() is an exponential factor.

Value

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: ExponentialAdditiveCooling(), ExponentialMultiplicativeCooling(), LogarithmicMultiplicativeCooling(), PowerAdditiveCooling(), TrigonometricAdditiveCooling()

Examples

parm<-function(x){function() {return(x)}}
lF<-list(Temp0=parm(100), Alpha=parm(1.01), CoolingPower=parm(2))
PowerMultiplicativeCooling(0, lF)
PowerMultiplicativeCooling(2, lF)

xegaPopulation documentation built on Aug. 22, 2025, 5:14 p.m.