High-dimensional function optimization with a self adaptive differential evolution

Author

Worasucheep, Chukiat

Author_Institution

Dept. of Math., King Mongkut´´s Univ. of Technol. Thonburi, Bangkok, Thailand

Volume

1

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

668

Lastpage

673

Abstract

A good optimization algorithm must be capable of handling high-dimensional problems, meaning that there are many decision variables to be optimized at the same time. The problems of this category are challenging. This paper tests the scalability of wDE, which is a differential evolution algorithm with self-adaptive parameters. The statistical results and convergence graphs from the experimentation using benchmark problems of 100-, 500-, and 2000-dimensions are analyzed and compared to three standard variants of differential evolution algorithm.

Keywords

evolutionary computation; global optimization; high-dimensional function optimization; self adaptive differential evolution algortihm; Algorithm design and analysis; Automatic testing; Benchmark testing; Convergence; Evolutionary computation; Genetic mutations; Mathematics; Neural networks; Robustness; Scalability; Differential Evolution; Evolutionary Algorithm; High dimensional; Scalability; Self-Adaptation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5357711

Filename

5357711