PSO-CGO
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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.
2011 ◽
Vol 2
(1)
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pp. 1-20
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2000 ◽
Vol 214
(9)
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2009 ◽
Vol 2
(3)
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pp. 121-140
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1999 ◽
Vol 20
(16)
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pp. 1752-1759
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2015 ◽
Vol 63
(4)
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pp. 677-707
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