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

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

Nuno Lourenço, Francisco Baptista Pereira
Copyright: © 2011 |Volume: 2 |Issue: 1 |Pages: 20
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781613508596|DOI: 10.4018/jncr.2011010101
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MLA

Lourenço, Nuno, and Francisco Baptista Pereira. "PSO-CGO: A Particle Swarm Algorithm for Cluster Geometry Optimization." IJNCR vol.2, no.1 2011: pp.1-20. http://doi.org/10.4018/jncr.2011010101

APA

Lourenço, N. & Pereira, F. B. (2011). PSO-CGO: A Particle Swarm Algorithm for Cluster Geometry Optimization. International Journal of Natural Computing Research (IJNCR), 2(1), 1-20. http://doi.org/10.4018/jncr.2011010101

Chicago

Lourenço, Nuno, and Francisco Baptista Pereira. "PSO-CGO: A Particle Swarm Algorithm for Cluster Geometry Optimization," International Journal of Natural Computing Research (IJNCR) 2, no.1: 1-20. http://doi.org/10.4018/jncr.2011010101

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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.

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