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Soft Computing
Erschienen in: Soft Computing 4/2017

19.08.2015 | Methodologies and Application

Novel discrete differential evolution methods for virtual tree pruning optimization

verfasst von: Damjan Strnad, Štefan Kohek

Erschienen in: Soft Computing | Ausgabe 4/2017

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Abstract

In this paper, we introduce two novel discrete differential evolution (DDE) methods for the optimization of simulated tree pruning within a software support tool for demonstration of tree training techniques. Therein, the pruning is posed as a combinatorial optimization problem of performing the cuts on a virtual tree model, whereby the objective function is defined by an empirical model of light interception. The proposed path-based and set-based DDE methods are closed to a discrete search domain under the implemented mutation operators. We compare both methods to several popular discrete optimization algorithms and a selection of efficient metaheuristics from continuous optimization, including existing DDE variants that map a discrete problem into continuous search space using real-valued solution encodings. We demonstrate that the path-based DDE achieves the best overall performance in the experiments on problem instances of different complexity classes.
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Metadaten
Titel
Novel discrete differential evolution methods for virtual tree pruning optimization
verfasst von
Damjan Strnad
Štefan Kohek
Publikationsdatum
19.08.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 4/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1827-x

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