.png){kind=icon}
返回
Novel discrete differential evolution methods for virtual tree pruning optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-015-1827-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Discrete differential evolution
Tree model
Pruning
Optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
.png){kind=icon}
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容