Search Results (3)

In this work, a novel multi-objective efficient global optimization (EGO) algorithm, namely GMOEGO, is presented by proposing an approach of available threads’ multi-objective infill criterion. The work applies the outstanding hypervolume-based expected improvement criterion to enhance the Pareto solutions in view of the accuracy and their distribution on the Pareto front, and the values of sophisticated hypervolume improvement (HVI) are technically approximated by counting the Monte Carlo sampling points under the modern GPU (graphics processing unit) architecture. As compared with traditional methods, such as slice-based hypervolume integration, the programing complexity of the present approach is greatly reduced due to such counting-like simple operations. That is, the calculation of the sophisticated HVI, which has proven to be the most time-consuming part with many objectives, can be light in programed implementation. Meanwhile, the time consumption of massive computing associated with such Monte Carlo-based HVI approximation (MCHVI) is greatly alleviated by parallelizing in the GPU. A set of mathematical function cases and a real engineering airfoil shape optimization problem that appeared in the literature are taken to validate the proposed approach. All the results show that, less time-consuming, up to around 13.734 times the speedup is achieved when appropriate Pareto solutions are captured. Full article

In order to overcome the problem of the low fitting accuracy of the expected improvement point infill criteria (EI) and the improved expected improvement point infill criteria (IEI), a high-precision surrogate modeling method based on the parallel multipoint expected improvement point infill criteria (PMEI) is presented in this paper for solving large-scale complex simulation problems. The PMEI criterion takes full advantage of the strong global search ability of the EI criterion and the local search ability of the IEI criterion to improve the overall accuracy of the fitting function. In the paper, the detailed steps of the PMEI method are introduced firstly, which can add multiple sample points in a single iteration. At the same time, in the process of constructing the surrogate model, it is effective to avoid the problem of the low fitting accuracy caused by adding only one new sample point in each iteration of the EI and IEI criteria. The numerical examples of the classical one-dimensional function and two-dimensional function clearly demonstrate the accuracy of the fitting function of the proposed method. Moreover, the accuracy of the multi-objective optimization surrogate model of a truck cab constructed by the PMEI method is tested, which proves the feasibility and effectiveness of the proposed method in solving high-dimensional modeling problems. All these results confirm that the Kriging model developed by the PMEI method has high accuracy for low-dimensional problems or high-dimensional complex problems. Full article

Public traffic has a great influence, especially with the background of COVID-19. Solving simulation-based optimization (SO) problem is efficient to study how to improve the performance of public traffic. Global optimization based on Kriging (KGO) is an efficient method for SO; to this end, this paper proposes a Kriging-based global optimization using multi-point infill sampling criterion. This method uses an infill sampling criterion which obtains multiple new design points to update the Kriging model through solving the constructed multi-objective optimization problem in each iteration. Then, the typical low-dimensional and high-dimensional nonlinear functions, and a SO based on 445 bus line in Beijing city, are employed to test the performance of our algorithm. Moreover, compared with the KGO based on the famous single-point expected improvement (EI) criterion and the particle swarm algorithm (PSO), our method can obtain better solutions in the same amount or less time. Therefore, the proposed algorithm expresses better optimization performance, and may be more suitable for solving the tricky and expensive simulation problems in real-world traffic problems. Full article