Machine Learning Assisted Evolutionary Multi-Objective Optimization [Guest Editorial

Abstract

Optimization and learning are two main paradigms of artificial intelligence in addressing complex real-world problems, with their respective focuses but frequently enhanced by each other. Evolutionary multi-objective optimization (EMO) algorithms are a family of nature-inspired algorithms widely used for solving multi-objective optimization problems (MOPs). Despite the great success achieved by the existing EMO algorithms, most of these algorithms have also encountered many challenges in terms of optimization performance and efficiency for solving complex MOPs such as large-scale MOPs, expensive MOPs, dynamic MOPs, and real-world MOPs. To cope with these challenges, there has been increasing interest in applying machine learning (ML) techniques to enhance the EMO algorithms. Specifically, ML techniques can be adopted to extract useful knowledge hidden in the data generated by EMO algorithms in the search process, which can be leveraged to assist different components in EMO algorithms in different ways, e.g., problem formulation, offspring generation, fitness evaluation, and/or environmental selection. These machine learning assisted components have substantially enhanced the ability of EMO algorithms in handling complex MOPs.