A surrogate-assisted evolution strategy for constrained multi-objective optimization
作者：
Highlights：
• New surrogate-assisted ES for constrained multi-objective optimization is developed.
• Surrogates are used to identify the most promising among many trial offspring.
• A radial basis function (RBF) model is used to implement the method.
• Method is tested on benchmark problems and manufacturing and robotics applications.
• Proposed method generally outperforms an ES and NSGA-II on the problems used.
摘要
•New surrogate-assisted ES for constrained multi-objective optimization is developed.•Surrogates are used to identify the most promising among many trial offspring.•A radial basis function (RBF) model is used to implement the method.•Method is tested on benchmark problems and manufacturing and robotics applications.•Proposed method generally outperforms an ES and NSGA-II on the problems used.
论文关键词：Multi-objective optimization,Constrained optimization,Evolution strategy,Surrogate,Metamodel,Radial basis function
论文评审过程：Received 28 September 2015, Revised 25 March 2016, Accepted 26 March 2016, Available online 29 March 2016, Version of Record 11 April 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.044
