This makes the algorithm more efficient and less sensitive to user defined parameters than canonical SA.
These are in the standard variant often selected on the basis of experience and experimentation (since optimal values are problem dependent), which represents a significant deficiency in practice.
The algorithm works by representing the parameters of the function to be optimized as continuous numbers, and as dimensions of a hypercube (N dimensional space).
Some SA algorithms apply Gaussian moves to the state, while others have distributions permitting faster temperature schedules.
Imagine the state as a point in a box and the moves as a rugby-ball shaped cloud around it.