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An approximate Annealing Search algorithm to global optimization and its connection to stochastic approximation

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2 Author(s)
Jiaqiao Hu ; Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, 11794, USA ; Ping Hu

The Annealing Adaptive Search (AAS) algorithm searches the feasible region of an optimization problem by generating candidate solutions from a sequence of Boltzmann distributions. However, the difficulty of sampling from a Boltzmann distribution at each iteration of the algorithm limits its applications to practical problems. To address this difficulty, we propose an approximation of AAS, called Model-based Annealing Random Search (MARS), that samples solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We present the global convergence properties of MARS by exploiting its connection to the stochastic approximation method and report on numerical results.

Published in:

Simulation Conference (WSC), Proceedings of the 2010 Winter

Date of Conference:

5-8 Dec. 2010