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Nash game based efficient global optimization for large-scale design problems | SpringerLink
Source: link.springer.com
Article summary:
1. This article discusses the use of Nash game based efficient global optimization for large-scale design problems.
2. It examines various methods such as Kriging, MOEA/D, and EGO parallelization to optimize expensive black-box functions.
3. The article also looks at multi-objective optimization criteria and stochastic black-box systems in order to improve the efficiency of global optimization.
Article analysis:
The article is generally reliable and trustworthy, as it provides a comprehensive overview of Nash game based efficient global optimization for large-scale design problems. The authors cite numerous sources to support their claims, including peer-reviewed journals and books, which adds credibility to their arguments. Furthermore, the authors provide detailed explanations of each method discussed in the article, making it easy for readers to understand the concepts presented.
However, there are some potential biases that should be noted. For example, the authors focus mainly on methods that have been successful in optimizing expensive black-box functions; they do not discuss any other methods or approaches that may be more suitable for certain types of problems. Additionally, while the authors provide an overview of multi-objective optimization criteria and stochastic black-box systems, they do not explore any counterarguments or alternative approaches that could be used instead. Finally, there is no mention of possible risks associated with using these methods; this should be addressed in future research on this topic.