.png){kind=icon}
返回
A parallel variable-fidelity algorithm for efficient constrained multi-objective aerodynamic design optimization
.png){kind=icon}
PRE
AI
DOI：10.1063/5.0219781.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Modern aerodynamic design optimization aims to discover optimal configurations using computational fluid dynamics under complex flow conditions, which is a typical expensive multi-objective optimization problem. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) combined with efficient global optimization is a promising method but requires enhanced efficiency and faces limitations in its application to multi-objective aerodynamic design optimization (MOADO). To address the issues, an efficient parallel MOEA/D assisted with variable-fidelity optimization (VFO) is proposed for solving MOADO, called the MOEA/D-VFO algorithm. Variable-fidelity surrogates are built for objectives and constraints, achieving higher accuracy using fewer high-fidelity samples and a great number of low-fidelity samples. By retaining more good candidates, the sub-optimization problems defined by decomposing original objectives are capable of discovering more favorable samples using MOEA/D, which prompts optimization convergence. A constraint-handling strategy is developed by incorporating the probability of feasibility functions in the sub-optimizations. The selection of new samples for parallel evaluation is improved by filtering out poor candidates and selecting effective promising samples, which improves the feasibility and diversity of solved Pareto solutions. A Pareto front (PF) can be efficiently found in a single optimization run. The proposed approach is demonstrated by four analytical test functions and verified by two aerodynamic design optimizations of airfoils with and without constraints, respectively. The results indicate that the MOEA/D-VFO approach can greatly improve optimization efficiency and obtain the PF satisfying constraints within an affordable computational budget.
Keyword:
IMPROVEMENT
CRITERION
期刊
IF:
4.3
论文数:
2.9W
被引数:
8.0W
机构
引用论文
Multi-objective optimization design of scramjet nozzle based on grey wolf optimization algorithm and kernel extreme learning machine surrogate model.png)
PHYSICS OF FLUIDS
PHYSICS OF FLUIDSIF4.3
PREAI
分享收藏
分享收藏Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization
PREAI
分享收藏Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids使用可变保真度代理模型和多级计算网格进行有效的空气动力学形状优化
OAAI
OAAI分享收藏Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models使用并行填充策略和替代模型对跨音速机翼进行有效的空气动力学形状优化
PREAI
分享收藏
查看更多内容