M.Sc., Faculty of Sciences, University of the Witwatersrand, 2011 / Abstract
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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/10679 |
| Date | 02 November 2011 |
| Creators | Moepya, Stephen Obakeng |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.002 seconds