A Supervised Surrogate-Assisted Evolutionary Algorithm for Complex Optimization Problems

Surrogate-assisted evolutionary algorithms (SAEAs), which use surrogate models to evaluate the individuals’ fitness, show high efficiency in solving complex optimization problems. In an SAEA, the solution quality and algorithm efficiency are the two most concerned performance measures. It is necessary to bring novel strategies to SAEAs to improve their solution quality and algorithm efficiency. In this article, we propose a supervised SAEA (SSAEA). The SSAEA takes the fitness evaluation accuracy (FEA) as a supervisor. Under the supervisor, the SSAEA brings two novel strategies, including the FEA-based surrogate model management strategy and the FEA-based new individual generation strategy. In our experiments, we compare the proposed SSAEA with several state-of-art SAEAs. The experimental results show that our proposed algorithm can obtain higher quality solutions in a shorter computational time.