An adaptive evolution control based on confident regions for surrogate-assisted optimization

In simulation-based optimization the objective function is often computationally expensive for many optimization problems. Surrogate-assisted optimization is therefore a major approach to efficiently solve them. One of the major issues of this approach is how to integrate the approximate models (surrogates or metamodels) in the optimization process. The challenge is to find the best trade-off between the quality (in terms of precision) of the provided solutions and the efficiency (in terms of execution time) of the resolution. In this paper, we investigate the evolution control that alternates between the simulator and the surrogate within the optimization process. We propose an adaptive evolution control mechanism based on the distance-based concept of confident regions. The approach has been integrated into an ANN-assisted NSGA-2 and experimented using the ZDT4 multi-modal benchmark function. The reported results show that the proposed approach outperforms two other existing ones.