In real life, some complex problems are multi-objective optimization problems. Most of the existing studies have focused on how to obtain the optimal solutions distributed on the whole Pareto-optimal frontier. However, in some fields such as industrial production, the decision-makers of enterprises usually care about what specific measures can maximize the comprehensive benefits of enterprises. Due to this kind of realistic demands, we prefer to find a small part of the optimal solutions according to the preference information suggested by the decision-makers rather than obtain all of the Pareto-optimal solutions. However, almost all of the existing methods only repeat calculation when they meet the scenario where the user-preferences change over time. To address the multi-objective optimization problem under the scenario with dynamic user-preferences information, we propose a MOEA/D-DPRE (multi-objective optimization algorithm based on dynamic preference information) algorithm in this paper, and its framework is inspired by the MOEA/D-PRE (decomposition user-preference multi-objective evolutionary) algorithm. We analyze the four position relations between the distribution region of the old preference weight vectors (old preference region), and we also present the distribution region of the new preference weight vectors (new preference region) and propose the different strategy to the different case respectively. When the preference information changes, the MOEA/D-DPRE can converge to the region of new interest by responding to the change of preference and the historical information. Experimental results show that the proposed method is better than the compared method in convergence speed and distribution under the scenario with dynamic user-preferences information.

在现实生活中，一些复杂的问题是多目标优化问题。现有的研究大多集中在如何获得分布在整个帕累托最优边界上的最优解。但是，在工业生产等一些领域，企业的决策者通常关心的是哪些具体措施可以使企业的综合效益最大化。由于这种现实需求，我们更愿意根据决策者建议的偏好信息找到一小部分最优解，而不是获得所有的帕累托最优解。然而，几乎所有现有的方法都只在遇到用户偏好随时间变化的场景时才重复计算。针对动态用户偏好信息场景下的多目标优化问题，本文提出了一种MOEA/D-DPRE（基于动态偏好信息的多目标优化算法）算法，其框架灵感来自于MOEA/D-PRE（分解用户偏好多目标进化）算法。我们分析了旧偏好权重向量的分布区域（旧偏好区域）之间的四个位置关系，并给出了新偏好权重向量的分布区域（新偏好区域），并针对不同的情况分别提出了不同的策略. 当偏好信息发生变化时，MOEA/D-DPRE 可以通过响应偏好的变化和历史信息来收敛到新的兴趣区域。