A data-driven evolutionary algorithm with multi-evolutionary sampling strategy for expensive optimization

Abstract

Surrogate-assisted evolutionary algorithms, which combine the powerful searching ability of evolutionary algorithms (EAs) with the predictive ability of surrogate models, are effective to solve expensive optimization problems. In this paper, an efficient data-driven EA based on multi-evolutionary sampling strategy (DDEA-MESS) is proposed. In DDEA-MESS, three sampling strategies (surrogate-assisted global search strategy, surrogate local search strategy and trust region search strategy) are combined together. The first strategy is focused on global exploration and the other two strategies are focused on local exploitation. Moreover, a probabilistic regulating mechanism for multi-strategy selection is proposed to avoid trapping in local optimum. Finally, a method of update the database is adapted to help algorithm escape from local optimum. The proposed algorithm is compared with other state-of-the-art algorithms on benchmark problems from 30 to 100 dimensions. The result indicate that DDEA-MESS has better performance for solving expensive optimization problems.