Adaptive cooperative co-evolution for large scale global optimization

Author

Wang, Yu ; Li, Zhengdong ; Zhengdong Li

Author_Institution

Dept. of Electron. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China

fYear

2010

fDate

28-30 Nov. 2010

Firstpage

178

Lastpage

181

Abstract

Large-scale global optimization (LSGO) is a very important and challenging task in optimization domain, which is embedded in many scientific and engineering applications. Previously, the cooperative co-evolution (CC) is a usual and effective choice for LSGO problems. In this paper, aim at more fully exploring the flexibility and potential of CC strategy, an adaptive CC (ACC) is designed to handle LSGO problems. The advantages of ACC compared with the classical CC strategies are experimentally verified on a set of widely used large scale function optimization problems.

Keywords

adaptive control; genetic algorithms; large-scale systems; LSGO problems; adaptive cooperative co-evolution; genetic algorithm; large scale global optimization; Algorithm design and analysis; Benchmark testing; Convergence; Evolutionary computation; Optimization; Technological innovation; Writing; adaptive; cooperative co-evolution; differential evolution; genetic algorithm; large scale global optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Computing and Telecommunications (YC-ICT), 2010 IEEE Youth Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-8883-4

Type

conf

DOI

10.1109/YCICT.2010.5713074

Filename

5713074