Simulated annealing control

It is probably correct to say that successful application of simulated annealing requires a clever definition ofndghborhoods in the configuration space (i.e., specification of individual "moves") and a ju dous variation of the control parameter p. In the case of metric spaces we would like to have a rationale for choosing an appropriate step size and for specifying a priori an effective cooling schedule. 

The aim of the optimisation is to determine the spatial pattern of retrofit flue gas desulphurisation FGD which, for a given total installed capacity of abatement, minimises the magnitude of the difference between the deposition loads and the critical loads for total sulphur deposition at the receptor sites. Such differences between deposition loads and ciritical loads are termed critical load exceedences. For a near continuous distribution of emission controls at about 50 power stations and 11 receptor sites, there are a large number of possible strategies to work through in an exhaustive analysis. The problem was solved using optimisation by simulated annealing [8-10], a specialised iterative improvement technique. 

None of these conclusions about optimal strategies were particularly counterintuitive or different from those which would have been generated fmm an exhaustive search of the large, but not unreasonably lai e, number of possibilities. They served to show that optimisation by simulated annealing could find reliable solutions in cases which could be checked manually. Furthermore they showed that a flexible representation of control strategies could be built to handle aU the features of the acid rain problem in Europe. This flexible representation was then extended to tackle more of the complexities of the situation which would take the... 

Strategies for the control of photochemical oxidants contain information not only on the extent of both hydrocarbons and NO, controls but their separate locations. Optimisation by simulated annealing has been applied to the investigation of optimal strategies for ozone control, involving the simultaneous abatement of hydrocarbons and NO, emissions in concert. This application involves the following stages ... 

Simulated annealing requires the control of the temperature during molecular dynamics. The three most commonly used methods are velocity scaling, Langevin dynamics, and temperature coupling. 

The effective use of temperature as a control factor in the Metropolis Monte Carlo process is the most important factor for the success of simulated annealing method. This has inspired an attempt to exploit temperature in a new way. Von Freyberg [21] has proposed a Simulated Shocking protocol for the efficient search for conformations of polypeptides. In this protocol, the temperature jumps between a very low (T = 5 K) and very high temperature... 

J. Kelly, B. Golden, A. Assad, Using simulated annealing to solve controlled rounding problems, ORSA Journal on Computing, 2 (1990) 174. 

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