Neighbor selection in simulated annealing algorithm
algorithm, artificial-intelligence, simulated-annealing
Solution
The latter is true: Only the acceptance probability is influenced by the temperature. The higher the temperature, the more "bad" moves are accepted to escape from local optima. If you preselect neighbors with low energy values, you'll basically contradict the idea of Simulated Annealing and turn it into a greedy search.
Pseudocode from Wikipedia:
s ← s0; e ← E(s) // Initial state, energy.
sbest ← s; ebest ← e // Initial "best" solution
k ← 0 // Energy evaluation count.
while k < kmax and e > emax // While time left & not good enough:
T ← temperature(k/kmax) // Temperature calculation.
snew ← neighbour(s) // Pick some neighbour.
enew ← E(snew) // Compute its energy.
if P(e, enew, T) > random() then // Should we move to it?
s ← snew; e ← enew // Yes, change state.
if enew < ebest then // Is this a new best?
sbest ← snew; ebest ← enew // Save 'new neighbour' to 'best found'.
k ← k + 1 // One more evaluation done
return sbest // Return the best solution found.
Problem
When picking a neighbor should the algorithm's temperature be considered? So for example if the temperature is high when picking a neighbor should be permutation be made? Or does the temperature only affect the acceptance probability?