Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10145929 | Information Sciences | 2019 | 17 Pages |
Abstract
Expected improvement (EI) is a popular infill criterion in Gaussian process assisted optimization of expensive problems for determining which candidate solution is to be assessed using the expensive evaluation method. An EI criterion for constrained expensive optimization (constrained EI) has also been suggested, which requires that feasible solutions exist in the candidate solutions. However, the constrained EI criterion will fail to work in case there are no feasible solutions. To address the above issue, this paper proposes a new EI criterion for highly constrained optimization that can work properly even when no feasible solution is available in the current population. The proposed constrained EI criterion can not only exploit local feasible regions, but also explore infeasible yet promising regions, making it a complete constrained EI criterion. The complete constrained EI is theoretically validated and empirically verified. Simulation results demonstrate that the proposed complete constrained EI is better than or comparable to five existing infill criteria.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ruwang Jiao, Sanyou Zeng, Changhe Li, Yuhong Jiang, Yaochu Jin,