Abstract
A new multi-objective optimization problem in presence of noise is formulated and called Multi-Noisy-Hard-objective Optimization Problem (MNHOP). Since considering the worst case performance is important in many real-world optimization problems, each solution of MNHOP is evaluated based on the upper bounds of noisy objective functions' values predicted statistically from multiple samples. Then an Evolutionary Multi-objective Optimization Algorithm (EMOA) based on Differential Evolution is applied to MNHOP. Three sample saving techniques, namely U-cut, C-cut, and re-sampling, are proposed and introduced into the EMOA for allocating its computing budget only to promising solutions. Finally, the effects of those techniques are examined through numerical experiments.
