.png){kind=icon}
返回
Multi-Strategy Enhanced Parrot Optimizer: Global Optimization and Feature Selection
.png){kind=icon}
OA
AI
DOI：10.3390/biomimetics9110662.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Optimization algorithms are pivotal in addressing complex problems across diverse domains, including global optimization and feature selection (FS). In this paper, we introduce the Enhanced Crisscross Parrot Optimizer (ECPO), an improved version of the Parrot Optimizer (PO), designed to address these challenges effectively. The ECPO incorporates a sophisticated strategy selection mechanism that allows individuals to retain successful behaviors from prior iterations and shift to alternative strategies in case of update failures. Additionally, the integration of a crisscross (CC) mechanism promotes more effective information exchange among individuals, enhancing the algorithm's exploration capabilities. The proposed algorithm's performance is evaluated through extensive experiments on the CEC2017 benchmark functions, where it is compared with ten other conventional optimization algorithms. Results demonstrate that the ECPO consistently outperforms these algorithms across various fitness landscapes. Furthermore, a binary version of the ECPO is developed and applied to FS problems on ten real-world datasets, demonstrating its ability to achieve competitive error rates with reduced feature subsets. These findings suggest that the ECPO holds promise as an effective approach for both global optimization and feature selection.
Keyword:
parrot optimizer
global optimization
feature selection
crisscross mechanism
metaheuristic algorithms
bionic algorithm
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
B
IF:
3.9
论文数:
3.3K
被引数:
5.1K
机构
引用论文
A comprehensive survey on recent metaheuristics for feature selection对最近用于特征选择的元启发式方法的综合调查.png)
NEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selectionSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Toward modeling and optimization of features selection in Big Data based social Internet of Things基于大数据的社交物联网特征选择建模与优化研究
PREAI
分享收藏分享收藏
查看更多内容