Only the acceptance probability in the simulated annealing algorithm is influenced by the temperature. The higher the temperature, the more "bad" moves are accepted to escape from local optima. If you select neighbors(preselected) with low energy values, you'll basically contradict the idea of Simulated Annealing and turn it into a greedy search.

And for better understanding of NEIGHBORHOOD SIZE IN THE SIMULATED ANNEALING ALGORITHM, refer the following link:

__https://pdfs.semanticscholar.org/8f55/6af294f39f03f585f0f269661dc50f472ce9.pdf__

If you are looking to learn more about Artificial Intelligence then visit this Artificial Intelligence Course which will cover topics like Simulated annealing algorithm Euclidean distance, Pearson correlation coefficient, Brute force search algorithms, Backtracking, Traveling salesman problem, NeuroEvolution of augmenting topologies, Fitness function, Resolution algorithm,k-nearest neighbors algorithm, Markov model, Genetic algorithm, deep first iterative deeping and many more.