Novel discrete differential evolution methods for virtual tree pruning optimization

详细信息    查看全文

• 作者：Damjan Strnad ; Štefan Kohek
• 关键词：Discrete differential evolution ; Tree model ; Pruning ; Optimization
• 刊名：Soft Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：21
• 期：4
• 页码：981-993
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1433-7479
• 卷排序：21

文摘

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.