ABSTRACT
Abstract ................................................................................................... 49 3.1 Introduction .................................................................................... 49 3.2 Evolutionary Cluster Optimization ................................................ 51 3.3 Microsolvation of Alkali-Metal Ions ............................................. 53
J. M. C. MARQUES1,*, W. S. JESUS1,2, F. V. PRUDENTE2, F. B. PEREIRA3,4, and N. LOURENÇO4,5
1CQC, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
2Instituto de Física, Universidade Federal da Bahia, 40, 170-115 Salvador, Brazil
3Instituto Superior de Engenharia de Coimbra, Quinta da Nora, 3030-199 Coimbra, Portugal
4Centro de Informática e Sistemas da Universidade de Coimbra (CISUC), 3030-290 Coimbra, Portugal
5Department of Informatics Engineering, University of Coimbra, 3030-290 Coimbra, Portugal
*Corresponding author. E-mail: qtmarque@ci.uc.pt
3.4 Binary Transition Metal Clusters ................................................... 61 3.5 New Self-Strategies for Global Optimization:
Application to Morse Clusters ....................................................... 63 3.6 Conclusions .................................................................................... 69 Acknowledgments ................................................................................... 70 Keywords ................................................................................................ 70 References ............................................................................................... 70
ABSTRACT
We review our work on the development of evolutionary algorithms (EAs) for revealing low-energy structures of atomic and molecular clusters. The application of EAs on the study of the microsolvation of alkali-metal ions with argon and assessing the chemical ordering of binary clusters of transition-metal elements is discussed. Additionally, we discuss the application of novel self-adaptive bioinspired algorithms to model cluster systems. Several adaptive strategies dealing both with control parameters and algorithmic components will be presented and some preliminary results are described and analyzed.
