Surrogate-assisted global transfer optimization based on adaptive sampling strategy

Surrogate Models have emerged as a useful technique to study system performance in engineering projects, especially engineering optimization. Previous research has focused on developing more efficient surrogate models and their application to practical problems. However, due to the scarcity of training data in the model and the lack of inheritance of similar information, the surrogate model of new projects is usually constructed from scratch, and the optimization effect of engineering design may not be satisfactory. As the need to rapidly design serialized products increases significantly, one potential solution is to transfer prior knowledge of similar models. In this study, a new surrogate-assisted global transfer optimization (SGTO) framework is proposed. The framework consists of three stages: space division, adaptive samples estimation and dynamic transfer allocation. The new promising samples were labeled by the error, predicted value, sample density of the interactive in-formation, and the anti-error deletion strategy was set. In this way, SGTO facilitates information transfer across projects, avoids learning new problems from scratch, and significantly reduces the computational burden. Through 17 benchmark cases and four engineering cases, the average performance of the framework is improved by 12.8%.