Exact optimization by means of sequentially adaptive Bayesian learning

Simulated annealing is a well-established approach to optimization that is robust for irregular objective functions. Recently it has been improved using sequential Monte Carlo. This paper presents further improvements that yield the global optimum with accuracy constrained only by the limitations of floating point arithmetic. Performance is illustrated using a standard set of six test problems in which simulated annealing has had mixed success. Our approach reliably finds the exact global optimum in all six cases, and with fewer function evaluations than competing simulated annealing algorithms. This approach is a specific case of the sequentially adaptive Bayesian learning algorithm, which uses feedback from particles to the design of the algorithm. The feature of this algorithm most critical to exact optimization is targeted tempering, a new technique developed in this paper.