A research for expensive many-objective optimization problem based on uncertainty of surrogate

Abstract

In surrogate assisted many objective optimization, conflicting uncertainties of surrogate between objective is a challenge. Hence, an many objective optimization algorithm with uncertainty of surrogate is proposed called, US-MOEA. The main work of this paper is as follows: first of all, infill criterion based on the uncertainty of predicted value is proposed to select promising solutions for re-evaluating by expensive optimization objective function. Then, in order to reduce the computational resources, a method based on non-dominated sorting is used to select some individual as train sample. In order to verify the effectiveness of proposed algorithm, the DTLZ and WFG test suits problem are applied and compare with five the-state-of-art algorithms proposed in recent years. The experimental results illustrate that the US-MOEA is an effectively method for solving expensive many objective optimization problems.

Keywords

evolutionary algorithm / expensive many objective optimization problem / surrogate / infill criterion / uncertainty