Abstract
In this paper we propose a new version of the Differential Evolution (DE) Algorithm for large scale optimization problems. The new algorithm, for exploration and localization of search, periodically uses topographical information on the objective function, in particular the k g -nearest neighbour graph. The algorithm is tested on hard practical problems from computational chemistry. These are the problems of semi-empirical many-body potential energy functions considered for carbon-carbon and silicon-silicon atomic interactions. The minimum binding energies of both carbon and silicon clusters consisting of upto 15 particles are reported.
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Ali, M.M., Törn, A. (2000). Optimization of Carbon and Silicon Cluster Geometry for Tersoff Potential using Differential Evolution. In: Floudas, C.A., Pardalos, P.M. (eds) Optimization in Computational Chemistry and Molecular Biology. Nonconvex Optimization and Its Applications, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3218-4_17
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DOI: https://doi.org/10.1007/978-1-4757-3218-4_17
Publisher Name: Springer, Boston, MA
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