Abstract
Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas static multimodal multiobjective evolutionary algorithms lack adaptability to dynamic changes. To address above challenge, this paper makes two primary contributions. First, we introduce a new benchmark suite of dynamic multimodal multiobjective test functions constructed by fusing the properties of both dynamic and multimodal optimization to establish a rigorous evaluation platform. Second, we propose a novel algorithm centered on a Clustering-based Autoencoder prediction dynamic response mechanism, which utilizes an autoencoder model to process matched clusters to generate a highly diverse initial population. Furthermore, to balance the algorithm's convergence and diversity, we integrate an adaptive niching strategy into the static optimizer. Empirical analysis on 12 instances of dynamic multimodal multiobjective test functions reveals that, compared with several state-of-the-art dynamic multiobjective evolutionary algorithms and multimodal multiobjective evolutionary algorithms, our algorithm not only preserves population diversity more effectively in the decision space but also achieves superior convergence in the objective space.
Abstract (translated)
动态多模态多目标优化面临着在时间变化的环境中同时跟踪多个等效帕累托最优集和保持种群多样性的双重挑战。然而，现有的动态多目标进化算法往往忽视了解决方案的模式性，而静态多模态多目标进化算法则缺乏对动态变化的适应能力。为解决上述难题，本文做出了两项主要贡献。 首先，我们引入了一套新的动态多模态多目标测试函数基准集，该基准集通过融合动态优化和多模态优化的特点建立了严格的评估平台。 其次，我们提出了一种基于聚类自编码器预测机制的新型算法。此算法利用自编码模型处理匹配的聚类以生成高度多样化的初始群体，并且为了平衡算法的收敛性和多样性，在静态优化器中集成了一种适应性利基策略。 通过对12个动态多模态多目标测试函数实例进行实证分析，结果表明与几种最先进的动态多目标进化算法和多模态多目标进化算法相比，我们的算法不仅更有效地在决策空间中保持了种群多样性，还在目标空间的收敛性方面取得了卓越的表现。
URL
https://arxiv.org/abs/2512.18947
