Abstract
To address the challenges of high-dimensional constrained optimization problems with expensive simulation models,a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling(SADE-MLS)is proposed.In SADE-MLS,differential evolution opera-tors are executed to generate numerous high-dimensional candidate points.To alleviate the curse of dimensionality,a Manifold Learning-based Sampling(MLS)mechanism is developed to explore the high-dimensional design space effectively.In MLS,the intrinsic dimensionality of the candidate points is determined by a maximum likelihood estimator.Then,the candidate points are mapped into a low-dimensional space using the dimensionality reduction technique,which can avoid signif-icant information loss during dimensionality reduction.Thus,Kriging surrogates are constructed in the low-dimensional space to predict the responses of the mapped candidate points.The candidate points with high constrained expected improvement values are selected for global exploration.Moreover,the local search process assisted by radial basis function and differential evolution is per-formed to exploit the design space efficiently.Several numerical benchmarks are tested to compare SADE-MLS with other algorithms.Finally,SADE-MLS is successfully applied to a solid rocket motor multidisciplinary optimization problem and a re-entry vehicle aerodynamic optimization problem,with the total impulse and lift to drag ratio being increased by 32.7％and 35.5％,respec-tively.The optimization results demonstrate the practicality and effectiveness of the proposed method in real engineering practices.
基金项目
National Natural Science Foundation of China(52272360)
National Natural Science Foundation of China(52232014)
National Natural Science Foundation of China(52005288)
National Natural Science Foundation of China(52201327)
Beijing Natural Science Foundation,China(3222019)
Beijing Institute of Technology Research Fund Program for Young Scholars,China(XSQD-202101006)
BIT Research and Innovation Promoting Project(2022YCXZ017)
NETL
NSTL
万方数据