Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the context of interactive EMOAs, preference information elicited from the DM during the optimization process can be leveraged to identify and discard irrelevant objectives, a crucial step when objective evaluations are computationally expensive. However, much of the existing literature fails to account for the dynamic nature of DM preferences, which can evolve throughout the decision-making process and affect the relevance of objectives. This study addresses this limitation by simulating dynamic shifts in DM preferences within a ranking-based interactive algorithm. Additionally, we propose methods to discard outdated or conflicting preferences when such shifts occur. Building on prior research, we also introduce a mechanism to safeguard relevant objectives that may become trapped in local or global optima due to the diminished correlation with the DM-provided rankings. Our experimental results demonstrate that the proposed methods effectively manage evolving preferences and significantly enhance the quality and desirability of the solutions produced by the algorithm.

翻译：进化多目标优化算法（EMOAs）被广泛应用于处理具有多个冲突目标的问题。最新研究表明，并非所有目标对决策者（DM）都具有同等重要性。在交互式EMOAs的背景下，优化过程中从决策者获取的偏好信息可用于识别并剔除无关目标——这一步骤在目标评估计算成本高昂时尤为关键。然而，现有文献大多未能考虑决策者偏好的动态特性，这些偏好可能在决策过程中不断演变并影响目标的相关性。本研究通过在基于排序的交互式算法中模拟决策者偏好的动态漂移来解决这一局限性。此外，我们提出了在发生偏好漂移时剔除过时或冲突偏好的方法。基于先前研究，我们还引入了一种保护机制，用于防止相关目标因与决策者提供的排序关联性减弱而陷入局部或全局最优解。实验结果表明，所提出的方法能有效管理动态演变的偏好，并显著提升算法生成解的质量与合意性。