A Self-Adaptive Evolutionary Algorithm for Cluster Geometry Optimization

Author

Pereira, Francisco B. ; Marques, Jorge M C

Author_Institution

Inst. Super. de Eng. de Coimbra, Coimbra

fYear

2008

fDate

10-12 Sept. 2008

Firstpage

678

Lastpage

683

Abstract

We propose a self-adaptive hybrid evolutionary algorithm for the optimization of Morse clusters. The approach relies on a two-phase local optimization method to efficiently guide search. Individuals encode its own penalty settings and the algorithm evolves them simultaneously with the search for low energy clusters. Results show that the approach is efficient, as it is able to discover all optimal solutions for Morse clusters between 41 and 80 atoms.

Keywords

Morse potential; adaptive systems; atomic clusters; evolutionary computation; molecular clusters; molecular configurations; optimisation; physics computing; search problems; Morse clusters; atomic clusters; cluster geometry optimization; low energy cluster search; molecular clusters; penalty settings; self-adaptive evolutionary algorithm; two-phase local optimization method; Clustering algorithms; Context modeling; Evolutionary computation; Geometry; Hybrid intelligent systems; Optimization methods; Potential energy; Rough surfaces; Surface roughness; Surface topography; cluster structure optimization; evolutionary computation; self-adaptation;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on

Conference_Location

Barcelona

Print_ISBN

978-0-7695-3326-1

Electronic_ISBN

978-0-7695-3326-1

Type

conf

DOI

10.1109/HIS.2008.96

Filename

4626709