Adversarial AutoEncoder-Based Large-Scale Dynamic Multiobjective Evolutionary Algorithm

Abstract

Dynamic multiobjective optimization problems (DMOPs) are often scaled to large-scale scenarios in real-world applications, which inevitably must face the triple challenges of massive search space, dynamic environmental changes and multiobjective conflicts simultaneously. This article proposes an adversarial autoencoder-based large-scale dynamic multiobjective evolutionary framework. It integrates deep generative modeling techniques and large-scale multiobjective evolutionary algorithms (LMOEAs) to solve large-scale DMOPs effectively and efficiently. Specifically, an adversarial autoencoder-based deep generative network training architecture is proposed for high-dimensional decision variables in large-scale DMOPs. It can transfer a generative model trained on Pareto-optimal solutions in the current environment to a new environment using only the auxiliary information exhibited through the movement trajectories of historical Pareto-optimal solutions, resulting in the generation of quality initial populations for the new environment. Meanwhile, any proven LMOEA can be integrated into the proposed framework without extensive modifications. Experimental results on a typical dynamic multiobjective test suite with problem settings from 30 to 1000 dimensions demonstrate that the optimization performance of the proposed framework outperforms existing state-of-the-art designs. Especially in large-scale scenarios, the proposed framework is considered superior in terms of solution quality and computational efficiency.