Adaptive Estimation of Student’s t-Distribution Algorithm for Large-Scale Global Optimization

In this paper, an adaptive estimation of student’s t-distribution algorithm (EDA-t) is proposed to deal with the large-scale global optimization problems. The proposed algorithm can not only obtain optimal solution with high precision, but also run faster than EDA and their variants. In order to reduce the number of the parameters in student’s t-distribution, we adapt its closed-form in latent space to replace it, and use the expectation maximization algorithm to estimate its parameters. To escape from local optimum, a new strategy adaptively tune the degree of freedom in the t-distribution is also proposed. As we introduce the technology of latent variable, the computational cost in EDA-t significantly decreases while the quality of solution can be guaranteed. The experimental results show that the performance of EDA-t is super than or equal to the state-of-the-art evolutionary algorithms for solving the large scale optimization problems.