Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions

This paper proposed a surrogate-assisted dominance-based multi-objective evolutionary algorithm to solve multi-objective computationally expensive problems with medium dimensions. Two infill criteria are collaboratively used to select promising individuals for exact evaluations. The convergence-based criterion is used to promote the exploitation of current promising areas. This criterion also considers the dispersion of selected solutions to exploit current non-dominant front. The diversity-based criterion is used to enhance the exploration of the population and enhance the accuracy of surrogate models. The feedback information from the convergence-based criterion is used to adjust the frequency of using the diversity-based criterion in order to reduce the consumed function evaluations. Benchmark functions with dimensions varying from 8 to 30 and a reactive power optimization problem are used to test the proposed algorithm. The experimental results demonstrate that the proposed algorithm significantly outperforms some state-of-the-art evolutionary algorithms on most problems.

Automated summary

This paper proposes a surrogate-assisted dominance-based multi-objective evolutionary algorithm, which can efficiently solve multi-objective computationally expensive problems with medium dimensions. By using convergence and diversity criteria collaboratively, the algorithm enhances the exploration of the population and improves the accuracy of surrogate models.