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Article summary:
1. An evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) is proposed for handling dynamic multiobjective optimization (DMO) environments.
2. LP takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes.
3. SDM identifies gaps in population distribution and employs a gap-filling technique to increase population diversity, while guiding rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence.
Article analysis:
The article is written in an objective manner, presenting both sides of the argument equally and providing evidence for its claims. The authors have provided detailed descriptions of their proposed algorithm, as well as extensive comparison studies with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems. The article does not appear to be biased or promotional in any way, nor does it contain any unsupported claims or missing points of consideration. The authors have also noted possible risks associated with their proposed algorithm, such as overfitting or premature convergence due to lack of diversity. All in all, this article appears to be trustworthy and reliable.