In this thesis we propose four new methods for solving constrained global optimization problems.
The first proposed algorithm is a differential evolution (DE) algorithm using penalty
functions for constraint handling. The second algorithm is based on the first DE algorithm
but also incorporates a filter set as a diversification mechanism. The third algorithm is also
based on DE but includes an additional local refinement process in the form of the pattern
search (PS) technique. The last algorithm incorporates both the filter set and PS into the DE
algorithm for constrained global optimization. The superiority of feasible points (SFP) and
the parameter free penalty (PFP) schemes are used as constraint handling mechanisms.
The new algorithms were numerically tested using two sets of test problems and the
results where compared with those of the genetic algorithm (GA). The comparison shows
that the new algorithms outperformed GA. When the new methods are compared to each
other, the last three methods performed better than the first method i.e. the DE algorithm.
The new algorithms show promising results with potential for further research.
Keywords: constrained global optimization, differential evolution, pattern search, filter
method, penalty function, superiority of feasible points, parameter free penalty.
ii
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/4733 |
| Date | 04 April 2008 |
| Creators | Kajee-Bagdadi, Zaakirah |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 909862 bytes, application/pdf, application/pdf |
Page generated in 0.007 seconds