TY - GEN
T1 - Surrogate-Assisted Hybrid Searching Method for High-Dimensional Expensive Optimization Problems
AU - Gao, Nannan
AU - Shi, Renhe
AU - Tai, Xinhui
AU - Ye, Nianhui
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - To address the challenges of intensive computation cost and poor convergence for high-dimensional expensive optimization problems, a surrogate-assisted hybrid searching method (SAHSM) is proposed in this paper. In SAHSM, the Leave-One-Out method is firstly carried out to adaptively choose the most promising radial basis function for the objective, which enhances the approximation performance of the surrogate. In order to enhance global exploration, the particle swarm optimization-based sampling mechanism is executed to generate offspring, and the best individual is selected as a global infill sample point. To accelerate convergence, a sequential quadratic programming method is adopted to find out the local optimum which is considered as a local infill sample point. During optimization, the surrogate is adaptively refined according to the global and local sampling mechanism, which improves the performance of the surrogate continuously. A number of high-dimensional benchmarks are used to illustrate the performance of SAHSM compared with ESAO a state-of-the-art optimization algorithm. Finally, SAHSM is applied to solve a 50-dimensional airfoil aerodynamic design optimization problem. The results show that the lift-drag ratio of the optimized airfoil has increased by 25.18% and 84.34% compared with ESAO and DE, which verifies the potential of SAHSM in engineering design.
AB - To address the challenges of intensive computation cost and poor convergence for high-dimensional expensive optimization problems, a surrogate-assisted hybrid searching method (SAHSM) is proposed in this paper. In SAHSM, the Leave-One-Out method is firstly carried out to adaptively choose the most promising radial basis function for the objective, which enhances the approximation performance of the surrogate. In order to enhance global exploration, the particle swarm optimization-based sampling mechanism is executed to generate offspring, and the best individual is selected as a global infill sample point. To accelerate convergence, a sequential quadratic programming method is adopted to find out the local optimum which is considered as a local infill sample point. During optimization, the surrogate is adaptively refined according to the global and local sampling mechanism, which improves the performance of the surrogate continuously. A number of high-dimensional benchmarks are used to illustrate the performance of SAHSM compared with ESAO a state-of-the-art optimization algorithm. Finally, SAHSM is applied to solve a 50-dimensional airfoil aerodynamic design optimization problem. The results show that the lift-drag ratio of the optimized airfoil has increased by 25.18% and 84.34% compared with ESAO and DE, which verifies the potential of SAHSM in engineering design.
KW - Aerodynamic optimization
KW - High-dimensional expensive problems
KW - Particle swarm optimization
KW - Radial basis function
UR - http://www.scopus.com/inward/record.url?scp=85199248703&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-0922-9_74
DO - 10.1007/978-981-97-0922-9_74
M3 - Conference contribution
AN - SCOPUS:85199248703
SN - 9789819709212
T3 - Mechanisms and Machine Science
SP - 1179
EP - 1192
BT - Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023
A2 - Tan, Jianrong
A2 - Liu, Yu
A2 - Huang, Hong-Zhong
A2 - Yu, Jingjun
A2 - Wang, Zequn
PB - Springer Science and Business Media B.V.
T2 - International Conference on Mechanical Design, ICMD 2023
Y2 - 20 October 2023 through 22 October 2023
ER -