Woodhouse, Robert (2010) An Empirical Study of Computational Optimisation Techniques for Microstrip Antennas. PhD thesis, University of York.
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.
There are many computational optimisation techniques, several of which have been applied to real world problems, such as wire antennas, building structures and turbine blade profiles. Some of these techniques are relatively well known within the scientific and engineering communities, such as genetic algorithms. Microstrip antennas (MSAs) are widely used, especially for mobile communications applications, due to their low profile and low cost. An empirical study was performed to ascertain which computational optimisation technique is the most efficient when optimising MSAs. In this context, the most efficient technique refers to the one that has the highest probability of finding a solution that meets the required specification when all techniques have the same computational time allocated to them. It was found that genetic algorithms, the simplest technique used, is the most efficient of those that were tried. The main reason for this was concluded to be due to the relatively low number of fitness evaluations performed per run. Other, more complex, techniques are likely to to be more efficient when more fitness evaluations (run time) are available.
|Item Type:||Thesis (PhD)|
|Keywords:||optimisation, microstrip, antennas, GA, CGP, GP, evolved, empirical|
|Academic Units:||The University of York > Electronics (York)|
|Depositing User:||Mr Robert Woodhouse|
|Date Deposited:||28 Jul 2011 11:48|
|Last Modified:||08 Sep 2016 12:20|