Moepya, Stephen Obakeng2011-11-022011-11-022011-11-02http://hdl.handle.net/10539/10679M.Sc., Faculty of Sciences, University of the Witwatersrand, 2011Abstract This dissertation is concerned with discrete global optimization of nonlinear problems. These problems are constrained and unconstrained and are not easily solvable since there exists multiplicity of local and global minima. In this dissertation, we study the current methods for solving such problems and highlight their ine ciencies. We introduce a new local search procedure. We study the rapidly-exploring random tree (RRT) method, found mostly in the research area of robotics. We then design two global optimization algorithms based on RRT. RRT has never been used in the eld of global optimization. We exploit its attractive properties to develop two new algorithms for solving the discrete nonlinear optimization problems. The rst method is called RRT-Optimizer and is denoted as RRTOpt. RRTOpt is then modi ed to include probabilistic elements within the RRT. We have denoted this method by RRTOptv1. Results are generated for both methods and numerical comparisons are made with a number of recent methods.enGlobal optimizationNonlinear integer programmingLocal searchMulti-startRapidly-exploring random treesThesis