Elite Multi-Group Differential Evolution

Author

Liang, J.J. ; Mao, X.B. ; Qu, B.Y. ; Niu, B. ; Chen, T.J.

Author_Institution

Sch. of Electr. Eng., Zhengzhou Univ., Zhengzhou, China

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

7

Abstract

An Elite Multi-Group Differential Evolution algorithm for unconstrained single objective optimization is proposed. In the novel algorithm, the population is divided into sub-groups with different parameters setting to balance the global and local search ability. The good information collected in the search process is exchanged among groups. Experiments are conducted on seven commonly used benchmark functions and two new constructed harder test functions which are useful to test the local search ability of the algorithms and the proposed algorithm shows its effectiveness and efficiency.

Keywords

evolutionary computation; optimisation; benchmark function; elite multigroup differential evolution algorithm; local search ability; search process; subgroups; unconstrained single objective optimization; Benchmark testing; Convergence; Educational institutions; Evolution (biology); Heuristic algorithms; Optimization; Vectors; Differential evolution; dynamic multi-swarm optimizor; evolutionary optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2012 IEEE Congress on

Conference_Location

Brisbane, QLD

Print_ISBN

978-1-4673-1510-4

Electronic_ISBN

978-1-4673-1508-1

Type

conf

DOI

10.1109/CEC.2012.6256568

Filename

6256568