Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.
Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human... 相似文献
In this paper, a 3?×?3-matrix representation of Birman?CWenzl?CMurakami (BWM) algebra has been presented. Based on which, unitary matrices A(??, ??1, ??2) and B(??, ??1, ??2) are generated via Yang?CBaxterization approach. A Hamiltonian is constructed from the unitary B(??, ??) matrix. Then we study Berry phase of the Yang?CBaxter system, and obtain the relationship between topological parameter and Berry phase. 相似文献
Aiming at the requirements of high-resolution imaging and high integration of the blast furnace radar,this paper presents a method for designing a wideband microstrip array antenna,which broadens the antenna bandwidth through the design of parasitic patches and air layers.By combining multi-input multi-output (MIMO) radar and synthetic aperture radar (SAR) imaging principles,a linear MIMO array is designed,and a near-field simulation imaging experiment is performed on the simulated feed line through the wave number domain imaging algorithm.Simulation results show that the gain of the main lobe of the antenna can reach 14.05 dBi,the reflection coefficient is less than -10 dB,the absolute bandwidth is 5.25 GHz,the operating frequency is 20.67~25.92 GHz,and the range resolution is increased to 3 cm compared with the existing blast furnace radar.The average error of the azimuth direction of the simulated material line imaging is 0.008 m,and the range direction is 0.0011 m.Compared with the traditional microstrip array antenna,this antenna effectively widens the bandwidth,and the range resolution is higher than that of the traditional blast furnace radar.It can accurately obtain the shape information on the simulated material line,and has an engineering application value for blast furnace surface monitoring. 相似文献