This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accur...This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation(MCS)to LHS for probabilistic trend calculations.The PSOmethod optimizes sample distribution,enhances global search capabilities,and significantly boosts computational efficiency.To validate its effectiveness,the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power.The performance was then compared with Latin Hypercubic Important Sampling(LHIS),which integrates significant sampling with theMonte Carlomethod.The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling.This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed.展开更多
As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully ...As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.展开更多
文摘This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation(MCS)to LHS for probabilistic trend calculations.The PSOmethod optimizes sample distribution,enhances global search capabilities,and significantly boosts computational efficiency.To validate its effectiveness,the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power.The performance was then compared with Latin Hypercubic Important Sampling(LHIS),which integrates significant sampling with theMonte Carlomethod.The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling.This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed.
基金Supported by National Natural Science Foundation of China (Grant Nos.51105040,11372036)Aeronautical Science Foundation of China (Grant Nos.2011ZA72003,2009ZA72002)+1 种基金Excellent Young Scholars Research Fund of Beijing Institute of Technology (Grant No.2010Y0102)Foundation Research Fund of Beijing Institute of Technology (Grant No.20130142008)
文摘As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.
