Neighborhood samples and surrogate assisted multi-objective evolutionary algorithm for expensive many-objective optimization problems

摘要

Many surrogate-assisted meta-heuristic algorithms have been proposed for single-objective expensive optimization problems, however, not so much attention has been paid to multi-objective expensive problems, especially for those with more than four objectives. In this paper, we use reference vector guided evolutionary algorithm (RVEA) to select suitable individuals, and radial basis function (RBF) networks are used to estimate the fitness of the original objective function to reduce the computational cost. These suitable individuals are optimized by the RBF network for several iterations. Then in surrogate model management, an infill strategy is proposed to select promising individuals for exact evaluations. Euclidean distance to origin or uncertainty is adaptively considered, according to the convergence degree of the current population in the infill strategy. The approximation uncertainty of each solution is calculated according to its distance to the modeling samples in the decision space. The experimental results on a number of many-objective optimization problems showed that the proposed method is competitive to three state-of-the-art algorithms for solving computationally expensive many-objective optimization problems. (C) 2021 Elsevier B.V. All rights reserved.