The solution of a variety of classes of global
optimisation problems
is required in the
implementation of a framework for sensitivity analysis in
multicriteria decision analysis. These
problems have linear constraints, some of which have a particular
structure, and
a variety of objective functions, which may be smooth or non-smooth. The
context in which they
arise implies a need for a single, robust solution method.
The literature contains few experimental results relevant to such a
need.
We report on our experience with the implementation of three
stochastic algorithms for global optimisation: the multi-level single
linkage algorithm,
the topographical algorithm and the simulated annealing algorithm.
Issues relating to their implementation and use to solve practical
problems are discussed.
Computational results suggest that, for the class of problems
considered, simulated annealing performs well.