Tianli Xiao

• PHD
• Anhui University of Technology

To solve the problem that the surrogate is usually limited to a single type, a multi-objective surrogate-based optimization method based on general improvement decomposition strategy is proposed. In this method, the model prediction value information is fully used to construct the general improvement multi-objective decomposition criterion and the...

In practical engineering, there are many challenges when solving multi-objective optimization problems, such as the black box characteristics and time-consuming evaluation. The traditional evolutionary optimization method is usually limited due to the expensive cost and the difficulty in obtaining solutions. To modify the deficiency, a data-driven...

Hybrid reliability analysis with mixed random and interval uncertainties is a significant challenge in the reliability assessment of engineering structures. The situation will be more intractable when involving incomplete interval data. To obtain reliable estimates of the failure probability limits, an effective parameter estimation method, integra...

Surrogate models have been proven to be powerful tools to alleviate the computational burden of structural reliability analysis. An appropriate surrogate model can guarantee prediction accuracy with limited samples. However, the traditional single modeling technique ignores the model‐form uncertainty due to insufficient knowledge of the physical sy...