Clustering-based Autoencoding for Dynamic Multiobjective Evolutionary Optimization

Abstract

Dynamic multiobjective optimization problems (DMOPs) usually contain multiple conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, transfer learning (TL)-based methods have been considered promising in solving DMOPs. However, most existing TL-based methods are computationally extensive and therefore time-consuming. In this paper, a clustering based autoencoding for DMOEA, called CAE-DMOEA, is proposed, which aims to generate a high-quality initial population to accelerate the evolutionary process and improve the optimization performance. In particular, by learning the mapping relationship between the regional centroids of the approximate Pareto-optimal solutions (POS) from the previous two environments, the CAE-DMOEA can effectively predict the regional centroids of the POS in the new environment, which helps to tracking the moving POS and enhance the optimization efficiency. To study the performance of the proposed method, extensive experiments have been carried out by comparing three state-of-the-art DMOEAs. The experimental results show that the overall performance of the CAE-DMOEA is superior to that of the compared algorithms.