Simulated annealing is a popular local search meta-heuristic used to address discrete
and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing
is that it provides a means to escape local optima by allowing hill-climbing moves (i.e., moves
which worsen the objective function value) in hopes of finding a global optimum. A brief history
of simulated annealing is presented, including a review of its application to discrete and continuous
optimization problems. Convergence theory for simulated annealing is reviewed, as well
as recent advances in the analysis of finite time performance. Other local search algorithms are
discussed in terms of their relationship to simulated annealing. The chapter also presents practical
guidelines for the implementation of simulated annealing in terms of cooling schedules,5