There are many multi-objective optimization problems in dynamic environments (DMOPs), characterized by conflicting objectives and changing objective functions over time. Additionally, the dynamic nature of DMOPs may lead to continuous changes in the pareto front. However, existing methods experience significant issues such as severe loss of diversity and slow convergence, which make it challenging to track the dynamic pareto front both accurately and efficiently. To tackle these issues, a knee point guided prediction model is proposed in this article, oriented to population composition structure, which has three original components: (1) Based on the movement trend of previous knee points, the knee point generation strategy combines neighborhood search and step size exploration to identify them in response to environmental changes; (2) Depended on knee point classification, historical non-dominated solutions are reused to cultivate high-quality individuals in new environments, thereby expediting population convergence; (3) Diversity individuals are generated through uniform interpolation between predicted knee points, which increases the distribution of the population. These three strategies are integrated to establish a comprehensive prediction model to direct the generation of initial populations in changing environments, enhancing both the diversity and convergence of population. The effectiveness analysis and performance comparisons with some state-of-the-art algorithms demonstrate that the proposed algorithm exhibits significant advantages in enhancing solution quality. Furthermore, experimental results based on real-world applications validate the practical significance of this study.
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