  We propose a global optimization algorithm based on the Sequential Monte
Carlo (SMC) sampling framework. In this framework, the objective function is
normalized to be a probabilistic density function (pdf), based on which a
sequence of annealed target pdfs is designed to asymptotically converge on the
set of global optima. A sequential importance sampling (SIS) procedure is
performed to simulate the resulting targets, and the maxima of the objective
function is assessed from the yielded samples. The disturbing issue lies in the
design of the importance sampling (IS) pdf, which crucially influences the IS
efficiency. We propose an approach to design the IS pdf online by embedding a
posterior exploration (PE) procedure into each iteration of the SMC framework.
The PE procedure can also explore the important regions of the parameter space
supported by the target pdf. A byproduct of the PE procedure is an adaptive
mechanism to design the annealing temperature schedule online. We compare the
performance of the proposed algorithm with those of several existing related
alternatives by applying them to over a dozen standard benchmark functions. The
result demonstrates the appealing properties of our algorithm.
