Masters thesis defence (Aleš Zamuda)
Date: 11. july 2008
Work:
A. Zamuda. Samoprilagajanje krmilnih parametrov pri algoritmu diferencialne evolucije za večkriterijsko optimizacijo: MSc thesis. Faculty of Electrical Engineering and Computer Science, 2008.
Abstract
We present a new differential evolution algorithm for multiobjective optimization controlled by the self-adaptation mechanism introduced in evolution stategies.
Algorithm design is presented with mathematically formal notation of algorithm's main parts and their assembly. The algorithm is described using a pseudocode. Computational complexity of the algorithm is given and some empirical measurement are given for evidence. Self-adaptation dynamics of control parameters is also studied.
State of the art test problems and quality indicators from literature for performance assessment of multiobjective optimization algorithms are listed. Using these, performance assessments of the algorithm are obtained showing numerous statistically significant improvements. Obtained results with the algorithm are also compared with related algorithms and statistically significant differences of the compared algorithms are pointed out on empirical results.
