Multi-Objective Bayesian Global Optimization for Continuous Problems and Applications
A common method to solve expensive function evaluation problem is using Bayesian Global Optimization, instead of Evolutionary Algorithms.
- Yang, K.
- 06 December 2017
- Thesis in Repository
A common method to solve expensive function evaluation problem is using Bayesian Global Optimization, instead of Evolutionary Algorithms. However, the execution time of multi-objective Bayesian Global Optimization (MOBGO) itself is still too long, even though it only requires a few function evaluations. The reason for the high cost of MOBGO is two-fold: on the one hand, MOBGO requires an infill criterion to be calculated many times, but the computational complexity of an infill criterion has so far been very high. Another reason is that the optimizer, which aims at searching for an optimal solution according to the surrogate models, is not sufficiently efficient. The main contributions of this thesis consist of 1. Decreased the computational complexity of a well-known infill criteria, Expected Hypervolume Improvement, into $n log (n)$ both in 2-D and 3-D cases; 2. Proposed a new criterion, Truncated Expected Hypervolume Improvement, to make full use of a-priori knowledge of objective functions, whenever it is available; 3. Proposed another infill criterion, Expected Hypervolume Improvement Gradient, to improve the convergence of the optimizer in MOBGO.