Abstract
In this paper we present an extended version of the two-stage lattice Monte Carlo approach for optimization of bimetallic nanoalloys proposed in (Mikhov R., Myasnichenko V., Kirilov L., Sdobnyakov N., Matrenin P., Sokolov D., Fidanova S. A Two-Stage Monte Carlo Approach for Optimization of Bimetallic Nanostructures. In: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), September 6–9, 2020. Sofia, Bulgaria, 285–288 (2020)). The two stages consist of simulated annealing on a larger lattice, followed by simulated diffusion. Both constituent algorithms are fairly similar in structure, but their combination was found to give significantly better solutions than simulated annealing alone. We test the proposed approach on a few examples and discuss how to tune the parameters of the algorithms so that they work together optimally.
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Acknowledgements
This work was partially funded by Russian Federation of Basic Research, project number 20-37-70007, by the Ministry of Science and Higher Education of the Russian Federation in the framework of the State Program in the Field of the Research Activity, project number 0817-2020-0007. Stefka Fidanova was supported by the Bulgarian NSF under the grant DFNI-DN 12/5 and by the Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and co-financed by the European Union through the European structural and Investment funds. Leoneed Kirilov and Rossen Mikhov were supported by the National Scientific Program “Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES)”, Ministry of Education and Science – Bulgaria, and by the Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and co-financed by the European Union through the European structural and Investment funds.
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Mikhov, R. et al. (2022). On the Problem of Bimetallic Nanostructures Optimization: An Extended Two-Stage Monte Carlo Approach. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2020. Studies in Computational Intelligence, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-030-82397-9_12
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