Stochastic filter methods for generally constrained global optimization (2016)
AuthorsPrice, C.J., Reale, M., Robertson, B.L.show all
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.
CitationPrice, C.J., Reale, M., Robertson, B.L. (2016) Stochastic filter methods for generally constrained global optimization. Journal of Global Optimization, (Early access online).
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