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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