.png){kind=icon}
返回
An enhanced surrogate-assisted differential evolution for constrained optimization problems
.png){kind=icon}
OA
AI
DOI：10.1007/s00500-023-07845-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The application of evolutionary algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate assisted evolutionary optimization procedure is proposed. The procedure combines the differential evolution method with a k-nearest neighbors (k-NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.
Keyword:
Constrained optimization problems
Evolutionary algorithms
Surrogate models
Constraint-handling techniques
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
Accumulated deformation and microstructure of deep silty clay subjected to two freezing-thawing cycles under cyclic loading在循环荷载下经历两次冻融循环的深部粉质黏土的累积变形和微观结构
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏分享收藏
查看更多内容