Comparison of local and global optimisation techniques for diffusion battery data analysis

The recovery of a size distribution from a set of diffusion battery deposition measurements is a typical example of a data inversion problem. A range of solution methods have been proposed and most rely on an iterative optimisation procedure. We used a non-linear regularisation approach and investigated the differences between local and global optimisation techniques. For local search methods the final solution depends on the starting point of the search and the probability of finding a good solution decreases as the search space becomes more complex. We present a global optimisation method based on simulated annealing. It is shown that good solutions can be consistently found using this method, but, that considerable computing time is required. The same solutions were found far more rapidly by performing repeats of a local search method from a range of start points. We conclude that the simulated annealing technique offers little advantage in the present application, although it has some attractive theoretical and practical properties and may prove to be useful in other aerosol data inversion problems.