Fast stochastic global optimization
Griff L. Bilbro · IEEE Transactions on Systems Man and Cybernetics · 1994
A new stochastic optimization strategy is introduced which cascades many Metropolis-like procedures to sample a Boltzmann distribution at fixed temperatures. Global optimization of an objective f(x) in a certain class is shown to require O((/spl Delta//T/sub low/)/sup 2/) computational effort where /spl Delta/=max/sub x,x'/(f(x)-f(x')) and T/sub low/ is a low enough temperature that the Boltzmann function of f at T/sub low/ acceptably small except for optimal x. This theoretical advantage is confirmed by experimental results which are presented for a problem in vector quantization and for seven standard test problems in nonlinear optimization.>