Teng, Nga Sing (2007) Function optimization using differential evolution without explicit parameter tuning. Masters thesis, Universiti Malaysia Sabah.

Text
mt0000000482.pdf Download (12MB)  Preview 
Abstract
This thesis investigated the possibility of developing a new version of the Differential Evolution (DE) algorithm that does not require explicit tuning of any of its evolutionary parameters. Similar to other types of canonical evolutionary algorithms, DE requires the user to manually handtune the Crossover Rate (Cr), Scaling Factor (F) and Number of Population (NP) using preliminary test runs prior to conducting the actual evolutionary optimization process. The main objective of this thesis is thus to design, implement and test different versions of DE which either uses a selfadaptive or fixed approach to determining these evolutionary parameters. To achieve this main objective, firstly, a standardized 3Parents DE (3PDE) algorithm is implemented and tested against the original 4Parents DE (4PDE). Next, the thesis investigates the removal of the first two evolutionary parameters, Cr and F, from the explicit handtuning requirement. A series of experiments are conducted involving selfadaptive Cr and F individually and in combination. Lastly, the thesis investigates the selfadaptation of NP using two different methodologies, which are absolute and relative encodings, to determine which is favorable. To analyze and compare the performances of the proposed algorithms, a suite of 20 wellknown numerical optimization benchmark test functions were used. Each experimental setup for each test function was repeated for 50 times using different seeds for statistical significance. A total of 7000 evolutionary runs were conducted in this thesis. The results are compared based firstly on the average solution quality in terms of optimization precision and secondly its convergence properties. In addition, statistical testing using twotailed ttests are performed at the end of each experimental phase to ascertain the significance of the findings. From the empirical investigations and statistical analysis conducted. a new DE algorithm employing selfadaptation of F and relative selfadaptation of NP with a fixed Cr (3PDESAFRel) yielded the best outcome in terms of removing the explicit handtuning of evolutionary parameters in DE. Moreover, this new DE algorithm not only performed comparably against the original DE but in fact outperformed DE very significantly in 7 of the 20 test functions in terms of average solution quality and 8 of the 20 test functions in terms of convergence as explained in last objective in this thesis.
Item Type:  Thesis (Masters) 

Keyword:  Differential Evolution (DE) algorithm, average solution quality, evolutionary parameter, 3Parents (3PDE), 4Parents (4PDE) 
Subjects:  Q Science > QC Physics 
Department:  SCHOOL > School of Engineering and Information Technology 
Depositing User:  SITI AZIZAH BINTI IDRIS  
Date Deposited:  01 Oct 2014 11:01 
Last Modified:  30 Oct 2017 11:38 
URI:  https://eprints.ums.edu.my/id/eprint/9604 
Actions (login required)
View Item 