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

作者：

Highlights：

• A multi-evolutionary sampling strategies algorithm is proposed.

• The database update strategy is presented to escape from local optima.

• Probabilistic regulating mechanism is proposed for multi-strategy selection.

摘要

•A multi-evolutionary sampling strategies algorithm is proposed.•The database update strategy is presented to escape from local optima.•Probabilistic regulating mechanism is proposed for multi-strategy selection.

论文关键词：Evolutionary algorithm,Data-driven,Surrogate model,Multi-evolutionary sampling strategy,Expensive problems

论文评审过程：Received 7 October 2021, Revised 30 December 2021, Accepted 9 February 2022, Available online 16 February 2022, Version of Record 26 February 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108436