为了提高射雾器的射程,同时提高Kriging代理模型的优化效率和精度,提出一种新的自适应代理优化算法,包括全局探索阶段和局部探索阶段。在全局探索阶段,提出改进的最大期望概率提高准则(Improved Probability Improvement,IPI)和并行加...为了提高射雾器的射程,同时提高Kriging代理模型的优化效率和精度,提出一种新的自适应代理优化算法,包括全局探索阶段和局部探索阶段。在全局探索阶段,提出改进的最大期望概率提高准则(Improved Probability Improvement,IPI)和并行加点策略;在局部探索阶段,通过最小响应面准则(Minimizing Prediction,MP)获取新样本点;各加点准则均采用差分进化算法进行寻优,并根据新样本点和已知样本点的关系来实现全局探索和局部探索的自适应切换,直至找到最优解。算例实验表明:和最大期望提高准则(Expected Improvement,EI)、多点期望提高准则(q-Expected Improvement,q-EI)、最大期望概率提高准则(Probability Improvement,PI)相比,优化算法的加点次数至少减少了6.32%,优化效率提高了11.61%以上,求解问题的有效性更好。采用优化算法对射雾器叶轮结构进行优化,射雾器射程提高了24.77%。展开更多
Network design problems （NDPs） have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermo...Network design problems （NDPs） have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem （MNDP） more complicated and difficult to solve. We adopt a surrogate-based optimization （SBO） framework to solve three featured categories of NDPs （continuous, discrete, and mixed-integer）. We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.展开更多
An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization(PSO) algorithm was constructed.As a new surrogate model technology,Kriging model has better fitting precision ...An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization(PSO) algorithm was constructed.As a new surrogate model technology,Kriging model has better fitting precision for nonlinear problem.The Kriging model was adopted to replace computer aided engineering(CAE) simulation as fitness function of multi-objective PSO algorithm,and the computation cost can be reduced greatly.By introducing multi-objective handling mechanism of crowding distance and mutation operator to multiobjective PSO algorithm,the entire Pareto front can be approximated better.It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.展开更多
基金Project supported by the Zhejiang Provincial Natural Science Foundation of China （No. LR17E080002）, the National Natural Science Foundation of China （Nos. 51508505, 71771198, 51338008, and 51378298）, the Fundamental Research Funds for the Central Universities, China （No. 2017QNA4025）, and the Key Research and Development Program of Zhejiang Province, China （No. 2018C01007）
文摘Network design problems （NDPs） have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem （MNDP） more complicated and difficult to solve. We adopt a surrogate-based optimization （SBO） framework to solve three featured categories of NDPs （continuous, discrete, and mixed-integer）. We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.
基金the National Natural Science Foundation of China (No. 50873060)
文摘An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization(PSO) algorithm was constructed.As a new surrogate model technology,Kriging model has better fitting precision for nonlinear problem.The Kriging model was adopted to replace computer aided engineering(CAE) simulation as fitness function of multi-objective PSO algorithm,and the computation cost can be reduced greatly.By introducing multi-objective handling mechanism of crowding distance and mutation operator to multiobjective PSO algorithm,the entire Pareto front can be approximated better.It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.
