  Estimation of distribution algorithms (EDA) as one of the EAs is a stochastic
optimization problem which establishes a probability model to describe the
distribution of solutions and randomly samples the probability model to create
offspring and optimize model and population. Reference Vector Guided
Evolutionary (RVEA) based on the EDA framework, having a better performance to
solve MaOPs. Besides, using the generative adversarial networks to generate
offspring solutions is also a state-of-art thought in EAs instead of crossover
and mutation. In this paper, we will propose a novel algorithm based on RVEA[1]
framework and using Distributional Adversarial Networks (DAN) [2]to generate
new offspring. DAN uses a new distributional framework for adversarial training
of neural networks and operates on genuine samples rather than a single point
because the framework also leads to more stable training and extraordinarily
better mode coverage compared to single-point-sample methods. Thereby, DAN can
quickly generate offspring with high convergence regarding the same
distribution of data. In addition, we also use Large-Scale Multi-Objective
Optimization Based on A Competitive Swarm Optimizer (LMOCSO)[3] to adopts a new
two-stage strategy to update the position in order to significantly increase
the search efficiency to find optimal solutions in huge decision space. The
propose new algorithm will be tested on 9 benchmark problems in Large scale
multi-objective problems (LSMOP). To measure the performance, we will compare
our proposal algorithm with some state-of-art EAs e.g., RM-MEDA[4], MO-CMA[10]
and NSGA-II.
