文献类型：期刊文献

英文题名：An adaptive surrogate-assisted particle swarm optimization for expensive problems

作者：Li, Xuemei[1];Li, Shaojun[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：25

期号：24

起止页码：15051

外文期刊名：SOFT COMPUTING

收录：;EI(收录号：20214111011529);WOS:【SCI-EXPANDED(收录号:WOS:000705718100001)】；

基金：The authors of this paper appreciate the support from the National Natural Science Foundation of China (Project No. 21676086).

语种：英文

外文关键词：Surrogate-assisted evolutionary algorithm; Ensemble model; Radial basis functions; Particle swarm optimization

摘要：To solve engineering problems with evolutionary algorithms, many expensive function evaluations (FEs) are required. To alleviate this difficulty, surrogate-assisted evolutionary algorithms (SAEAs) have attracted increasingly more attention in both academia and industry. Most existing SAEAs either waste computational resources due to the lack of accuracy of the surrogate model or easily fall into the local optimum as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization algorithm. In the proposed algorithm, a surrogate model is adaptively selected from a single model and an ensemble model by comparing the best existing solution and the latest obtained solution. Additionally, a model output criterion based on the standard deviation is suggested to improve the stability and generalization ability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within a limited number of FEs. Moreover, the performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.