Co-evolutionary global optimization algorithm

Author

Iwamatsu, Masao

Author_Institution

Dept. of Inf. & Comput. Eng., Kisarazu Nat. Coll. of Technol., Chiba, Japan

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1180

Lastpage

1184

Abstract

A hybrid global optimization method, the co-evolutionary global optimization algorithm, is proposed, which utilizes the self-organized critical state as a means of diversification of search and the traditional conjugate gradient local minimization method as a means of intensification of search. The former has been recently used by Boettcher and Percus (2000) to solve discrete combinatorial optimization problems. The proposed method has been tested to locate the lowest energy conformation of atomic clusters. It was found that the method was effective not only to locate the lowest energy state but also to enumerate all the low-lying metastable states

Keywords

atomic clusters; conjugate gradient methods; evolutionary computation; metastable states; molecular electronic states; optimisation; physics computing; search problems; self-adjusting systems; atomic clusters; coevolutionary global optimization algorithm; conjugate gradient local minimization method; discrete combinatorial optimization problems; hybrid global optimization method; low-lying metastable states; lowest energy conformation; search diversification; search intensification; self-organized critical state; Algorithm design and analysis; Artificial intelligence; Cities and towns; Educational institutions; Energy states; Metastasis; Minimization methods; Optimization methods; Partitioning algorithms; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004410

Filename

1004410