Expensive Optimization

Abstract: In some expensive multiobjective optimization problems (MOPs), several function evaluations can be carried ou in a batch way. Therefore, it is very desirable to develop methods which can generate multiple test points simultaneously. This paper proposes such a method, called MOEAD-EGO, fordealing with expensive multiobjective optimization. MOEADEGO decomposes an MOP in question into a number of singleobjective optimization subproblems. A predictive model is built for each subproblem based on the points evaluated so far. Effort has been made to reduce the overhead for modeling and to improve the prediction quality. At each generation, MOEA/D is used for maximizing the expected improvement metric values of all the subproblems, and then several test points are selected for evaluation. Extensive experimental studies have been carried out to investigate the ability of the proposed algorithm