A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning

Author

Wang, Hui ; Wu, Zhijian ; Rahnamayan, Shahryar ; Kang, Lishan

Author_Institution

State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China

fYear

2009

fDate

Nov. 30 2009-Dec. 2 2009

Firstpage

1090

Lastpage

1095

Abstract

In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems-A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100, 200, and 500 in order to compare with the results of other algorithms which are participating in this workshop. Current work is based on opposition-based differential evolution (ODE) and our previous work, accelerated PSO by generalized OBL.

Keywords

evolutionary computation; learning (artificial intelligence); continuous optimization problems; current workshop; evolutionary algorithm; generalized opposition-based learning; metaheuristics; opposition-based differential evolution; scalability test; Acceleration; Benchmark testing; Chromium; Design optimization; Evolutionary computation; Intelligent systems; Life estimation; Robustness; Scalability; System testing; Differential Evolution; Evolutionary Algorithms; Large-Scale Optimization; Opposition-Based Differential Evolution; Opposition-Based Learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on

Conference_Location

Pisa

Print_ISBN

978-1-4244-4735-0

Electronic_ISBN

978-0-7695-3872-3

Type

conf

DOI

10.1109/ISDA.2009.216

Filename

5364196