Incremental learning-inspired mating restriction strategy for Evolutionary Multiobjective Optimization

Abstract

The prior knowledge from the problem property can boost the evolutionary multiobjective optimization (EMO). The existing machine learning model for knowledge mining in the EMO has led to enhanced performance on multiobjective optimization problems with complicated Pareto fronts (MOPs-cPF), but the high computational cost resulting from model training should not be taken as a natural expense. To overcome this drawback, this paper proposes an incremental learning-inspired mating restriction strategy (ILMR) for solving MOP-cPF efficiently. In ILMR, a mating restriction is implemented based on an incremental learning model that establishes the mating pool for each solution in the population and incrementally updates as the population evolves. Specifically, it consists of two interdependent parts, i.e., a learning module and a forgetting module. In one evolutionary cycle, the learning module is used to learn new knowledge from the high-quality offspring solutions, while the forgetting module is utilized to remove the information provided by relatively poor solutions in the population. Moreover, a multiobjective evolutionary algorithm with ILMR, named MEILM, is proposed and compared with six state-of-the-art algorithms on a variety of MOP-cPF. The experimental results show that there are significant improvements benefitting from the proposed mating restriction strategy.