A Classifier-Assisted Level-Based Learning Swarm Optimizer for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to solve complex and computationally expensive optimization problems. However, most existing SAEAs suffer from performance degradation with the dimensionality increasing. To solve this issue, this article proposes a classifier-assisted level-based learning swarm optimizer on the basis of the level-based learning swarm optimizer (LLSO) and the gradient boosting classifier (GBC) to improve the robustness and scalability of SAEAs. Particularly, the level-based learning strategy in LLSO has a tight correspondence with the classification characteristic by setting the number of levels in LLSO to be the same as the number of classes in GBC. Together, the classification results feedback the distribution of promising candidates to accelerate the evolution of the optimizer, while the evolved population helps to improve the accuracy of the classifier. To select informative and valuable candidates for real evaluations, we devise an L1-exploitation strategy to extensively exploit promising areas. Then, the candidate selection is conducted between the predicted L1 offspring and the already real-evaluated L1 individuals based on their Euclidean distances. Extensive experiments on commonly used benchmark functions demonstrate that the proposed optimizer can achieve competitive or better performance with a very small training dataset compared with three state-of-the-art SAEAs.

Automated summary

A new classifier-assisted level-based learning swarm optimizer is proposed in this study, which combines the level-based learning strategy and gradient boosting classifier to enhance the robustness and scalability of SAEAs. Experimental results demonstrate that the proposed optimizer outperforms three state-of-the-art SAEAs with a small training dataset.