Extending oscars-ii to generally constrained global optimization: Extending oscars-ii to generally constrained

A derivative free method for generally constrained global optimization is described. A non-smooth merit function with one parameter is used. When this parameter equals the optimal objective function value f ∗ , the merit function becomes an exact penalty function. The method estimates f ∗ , avoiding the need for it to be supplied. The method randomly samples the region satisfying the simple bounds from time to time, ensuring convergence almost surely. Other samples are drawn randomly from smaller regions considered promising. Numerical testing is done using a variety of bound constrained problems and generally constrained problems from the G-suite and elsewhere. Results show the method is competitive in practice. They also show that the method performs better when it estimates the optimal objective function value than when the actual value is used.