| dc.description.abstract | Evolutionary algorithms (EAs), such as the differential evolution (DE) algorithm, suffer
from high computational time due to large population size and nature of evaluation, to
mention two major reasons. The micro-EAs employ a very small population size, which
can converge to a reasonable solution quicker; while they are vulnerable to premature
convergence as well as high risk of stagnation. One approach to overcome the stagnation
problem is increasing the diversity of the population. In this thesis, a micro-differential
evolution algorithm with vectorized random mutation factor (MDEVM) is proposed, which
utilizes the small size population benefit while preventing stagnation through diversification
of the population. The following contributions are conducted related to the micro-DE
(MDE) algorithms in this thesis: providing Monte-Carlo-based simulations for the proposed
vectorized random mutation factor (VRMF) method; proposing mutation schemes
for DE algorithm with populations sizes less than four; comprehensive comparative simulations
and analysis on performance of the MDE algorithms over variant mutation schemes,
population sizes, problem types (i.e. uni-modal, multi-modal, and composite), problem
dimensionalities, mutation factor ranges, and population diversity analysis in stagnation
and trapping in local optimum schemes. The comparative studies are conducted on the
28 benchmark functions provided at the IEEE congress on evolutionary computation 2013
(CEC-2013) and comprehensive analyses are provided. Experimental results demonstrate
high performance and convergence speed of the proposed MDEVM algorithm over variant
types of functions. | en |
