2 ALGORITHMS FOR GLOBAL OPTIMIZATION OF GENERAL NLP PROBLEMS

After a brief overview of the methods from the literature, two new alg orithms (zooming and domain elimination) for global optimization of ge neral NLP problems are introduced. Operations analysis and stopping cr iteria for the methods are discussed. Numerical evaluation of the meth ods is carried out using a set of mathematical programming test proble ms. Performance of the methods is compared with the Controlled Random Search (CRS) and the Simulated Annealing (SA) methods. The methods are superior to SA for the test problems, as they are more robust, effici ent and accurate. The CRS is more efficient than the new methods; howe ver, it is applicable to unconstrained problems only. Therefore, it is concluded that the new methods are useful for engineering optimizatio n applications.