Stochastic filter methods for generally constrained global optimization
A lter based template for bound and otherwise constrained global op- timization of non-smooth black-box functions is presented. The constraints must include nite upper and lower bounds, and can include nonlinear equality and inequality constraints. Almost sure convergence is shown for a wide class of al- gorithms conforming to this template. An existing method for bound constrained global optimization (oscars) is easily modi ed to conform to this template. Nu- merical results show the modi ed oscars is competitive with other methods on test problems including those listed by Koziel and Michalewicz.