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PSO-CGO: A Particle Swarm Algorithm for Cluster Geometry Optimization

Author

Listed:
  • Nuno Lourenço

    (Centro de Informática e Sistemas da Universidade de Coimbra (CISUC), Portugal)

  • Francisco Baptista Pereira

    (Centro de Informática e Sistemas da Universidade de Coimbra (CISUC) and Instituto Superior de Engenharia de Coimbra, Portugal)

Abstract

In this paper the authors present PSO-CGO, a novel particle swarm algorithm for cluster geometry optimization. The proposed approach combines a steady-state strategy to update solutions with a structural distance measure that helps to maintain population diversity. Also, it adopts a novel rule to update particles, which applies velocity only to a subset of the variables and is therefore able to promote limited modifications in the structure of atomic clusters. Results are promising, as PSO-CGO is able to discover all putative global optima for short-ranged Morse clusters between 30 and 50 atoms. A comprehensive analysis is presented and reveals that the proposed components are essential to enhance the search effectiveness of the PSO.

Suggested Citation

  • Nuno Lourenço & Francisco Baptista Pereira, 2011. "PSO-CGO: A Particle Swarm Algorithm for Cluster Geometry Optimization," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 2(1), pages 1-20, January.
    • Handle: RePEc:igg:jncr00:v:2:y:2011:i:1:p:1-20
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    Cited by:

    1. Locatelli, Marco & Schoen, Fabio, 2012. "Local search based heuristics for global optimization: Atomic clusters and beyond," European Journal of Operational Research, Elsevier, vol. 222(1), pages 1-9.

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