APPLICATION OF STOCHASTIC GLOBAL OPTIMIZATION ALGORITHMS TO PRACTICALPROBLEMS

We describe global optimization problems from three different fields r epresenting many-body potentials in physical chemistry, optimal contro l of a chemical reactor, and fitting a statistical model to empirical data. Historical background for each of the problems as well as the pr actical significance of the first two are given. The problems are solv ed by using eight recently developed stochastic global optimization al gorithms representing controlled random search (4 algorithms), simulat ed annealing (2 algorithms), and clustering (2 algorithms). The result s are discussed, and the importance of global optimization in each res pective field is focused.