Simulated annealing is a method for exploring a problem space. The heuristic is that at the beginning of the exploration you start with a random solution i and a temperature T. At each step along the way you try to find a better value for F(i) by picking a neighbor j and evaluating F(j).
The twist in the algorithm is that depending on the temperature T, you might decide to move from i to j even if F(j) < F(i). The goal is to find a global maximum, not just a local maximum, so you’re willing to “explore” a less desirable result - initially with a high probability, but as the temperature decreases, then less and less until the system is cooled.
The paper is by Kirkpatrick et al, 1983, which can be found among other places on Semantic Scholar.
@ARTICLE{Kirkpatrick83optimizationby, author = {S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi}, title = {Optimization by simulated annealing}, journal = {SCIENCE}, year = {1983}, volume = {220}, number = {4598}, pages = {671–680} }