Approximation of the distribution of convergence times for stochastic global optimisation

Authors:
G. R. Wood;D. L. J. Alexander;D. W. Bulger
Affiliations:
Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand/Corresponding author (E-mail: G.R.Wood@massey.ac.nz);Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand/;Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand/
Venue:
Journal of Global Optimization
Year:
2002

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Abstract

How long should we run a stochastic global optimisation algorithm such as simulated annealing? How should we tune such an algorithm? This paper proposes an approach to the study of these questions through successive approximation of a generic stochastic global optimisation algorithm with a sequence of stochastic processes, culminating in a backtracking adaptive search process. Our emerging understanding of backtracking adaptive search can thus be used to study the original algorithm. The first approximation, the averaged range process, has the same expected number of iterations to convergence as the original process.