Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm

TL;DR

This paper introduces a clustering-based transfer learning approach using autoencoders for dynamic multimodal multiobjective optimization, improving diversity and convergence in time-varying environments.

Contribution

It presents a new benchmark suite and a novel autoencoder-based algorithm with adaptive niching for enhanced performance in dynamic multimodal multiobjective problems.

Findings

Outperforms state-of-the-art algorithms in diversity preservation.

Achieves superior convergence in objective space.

Effective handling of dynamic and multimodal challenges.

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.