A survey on pareto front learning for multi-objective optimization

Multi-objective optimization (MOO) is challenging since it needs to deal with multiple conflicting objectives. Multi-objective evolutionary algorithms (MOEAs) are the mainstream methods to solve MOO over the last two decades. Pareto Front Learning (PFL) is a new concept proposed to solve MOO in recent years. Its main idea is to use hypernetworks (HN) to effectively approximate the Pareto front. This paper reviews most PFL algorithms and divides them into two main categories. The first category is Pareto hypernetwork-based (PHN-based) PFL, introducing different frameworks of hypernetworks into PFL. Specifically, it includes different methods for aggregating loss functions and optimizing neural network parameters. The second category is HV-based PFL, which mainly uses hypervolume (HV) to optimize the hypernetwork. In addition to these two categories, this paper also reviews some PFL algorithms for solving specific problems such as multi-objective combinatorial optimization (MOCO). Lastly, the possible research directions of PFL are discussed.