Title :
Co-evolutionary global optimization algorithm
Author_Institution :
Dept. of Inf. & Comput. Eng., Kisarazu Nat. Coll. of Technol., Chiba, Japan
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1004410
