Localization for solving noisy multi-objective optimization problems.

This paper investigates the use of a framework of local models in the context of noisy evolutionary multi-objective optimization. Within this framework, the search space is explicitly divided into several nonoverlapping hyperspheres. A direction of improvement, which is related to the average performance of the spheres, is used for moving solutions within each sphere. This helps the local models to filter noise and increase the robustness of the evolutionary algorithm in the presence of noise. A wide range of noisy problems we used for testing and the experimental results demonstrate the ability of local models to better filter noise in comparison with that of global models.