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Inorganics 2017, 5(4), 64; https://doi.org/10.3390/inorganics5040064

Improved Cluster Structure Optimization: Hybridizing Evolutionary Algorithms with Local Heat Pulses

1
Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, NJ 08544-5263, USA
2
Institute for Physical Chemistry, Christian-Albrechts-University, 24098 Kiel, Germany
*
Author to whom correspondence should be addressed.
Received: 10 September 2017 / Revised: 24 September 2017 / Accepted: 26 September 2017 / Published: 29 September 2017
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Abstract

Cluster structure optimization (CSO) refers to finding the globally minimal cluster structure with respect to a specific model and quality criterion, and is a computationally extraordinarily hard problem. Here we report a successful hybridization of evolutionary algorithms (EAs) with local heat pulses (LHPs). We describe the algorithm’s implementation and assess its performance with hard benchmark CSO cases. EA-LHP showed superior performance compared to regular EAs. Additionally, the EA-LHP hybrid is an unbiased, general CSO algorithm requiring no system-specific solution knowledge. These are compelling arguments for a wider future use of EA-LHP in CSO. View Full-Text
Keywords: evolutionary algorithms; genetic algorithms; heat pulses; global optimization; cluster structure optimization evolutionary algorithms; genetic algorithms; heat pulses; global optimization; cluster structure optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Dieterich, J.M.; Hartke, B. Improved Cluster Structure Optimization: Hybridizing Evolutionary Algorithms with Local Heat Pulses. Inorganics 2017, 5, 64.

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