Offspring regeneration method based on bi-level sampling for large-scale evolutionary multi-objective optimization

In evolutionary multi-objective optimization, the size of search space exponentially expands as the number of decision variables increases, which makes the generation of promising candidate solutions more difficult. For this, in this paper, we propose a bi-level offspring generation architecture, together with a deep offspring sampling method. The offspring generation process is divided into two phases. The first phase uses the general genetic operators to generate the offspring, and then in the second phase, the selected non-dominated solutions are utilized by the proposed deep sampling method to produce offspring again. Specifically, the proposed deep sampling method makes use of the selected non-dominated solutions to establish search directions at first, then solutions are sampled on them. It is expected to take advantage of both offspring generation schemes, thereby balancing the diversity and convergence of the population. Existing large-scale evolutionary algorithms can easily be extended to our proposed bi-level architecture. The experimental results demonstrate the significant advantages of the proposed architecture and sampling method, in comparison with several state-of-the-art large-scale multi∖many-objective optimization problems in solving LSMOPs with up to 5000 decision variables.