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混沌扩散受限聚集增强型灰狼优化器：洞察、分析、二值化和特征选择 - 胡 - 2022 - 国际智能系统杂志 - Wiley 在线图书馆
Source: onlinelibrary.wiley.com
Article summary:
1. This article presents a new variant of Grey Wolf Optimizer (GWO) called SCGWO, which combines GWO with an improved diffusion strategy and Chaos Local Search (CLS) mechanism to overcome performance limitations.
2. The proposed SCGWO method is compared with various algorithms on benchmark functions of single-peak, multi-modal, and composite types to demonstrate its effectiveness.
3. Experiments show that the established SCGWO algorithm has significant advantages in dealing with single-peak, multi-modal, and composite functions, as well as finding an approximate optimal feature subset when applied to a set of real datasets from the UCI Machine Learning Repository for feature selection tasks.
Article analysis:
The article is generally reliable and trustworthy in terms of its content and sources. It provides detailed information about the proposed SCGWO algorithm and its comparison with other algorithms on benchmark functions of different types. The authors also provide evidence for their claims by conducting experiments on real datasets from the UCI Machine Learning Repository for feature selection tasks.
However, there are some potential biases in the article that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to solving the problem addressed in this paper. Additionally, they do not discuss any possible risks associated with using their proposed algorithm or any potential drawbacks that could arise from its use. Furthermore, while they present evidence for their claims through experiments conducted on real datasets from the UCI Machine Learning Repository, they do not provide any evidence for how their proposed algorithm performs against other existing algorithms or approaches used to solve similar problems.
In conclusion, while this article is generally reliable and trustworthy in terms of its content and sources, it does have some potential biases that should be noted when evaluating its trustworthiness and reliability.