Many-Objective Estimation of Distribution Optimization Algorithm Based on WGAN-GP

Understand

Estimation of distribution algorithms (EDA) are stochastic optimization algorithms.

• EDA establishes a probability model to describe the distribution of solution from the perspective of population macroscopically by statistical learning method, and then randomly samples the probability model to generate a new population.
• EDA can better solve multi-objective optimal problems (MOPs).
• However, the performance of EDA decreases in solving many-objective optimal problems (MaOPs), which contains more than three objectives.