Abstract
The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.
Highlights
The application of surrogate models has effectively enhanced optimization performance in many engineering design fields [1].The surrogate models, which approximately replace the complex black-box functions, can express a practical problem in a simple form and make researchers grasp characteristics of a primitive function step-by-step
For the actual kriging-based single-objective black-box constrained objective optimization problem, if we can obtain l mutually independent sampling points with greater potential value in one cycle while ensuring that certain accuracy requirements are met, the time consumption of the entire optimization process will be reduced by nearly l times under the condition that the maximum expensive times remain unchanged
For the kriging approximate function, large estimated root mean square error (RMSE) is helpful for krigingassisted multi-objective constrained global optimization (KMCGO) to explore some undeveloped regions so as to further enhance the possibility of obtaining the global optimal solution
Summary
Yaohui Li 1,2 , Jingfang Shen 2 , Ziliang Cai 1, *, Yizhong Wu 3 and Shuting Wang 3. This paper is an extended version of our paper published in WCGO 2019, the 6th international conference on Optimization of Complex Systems: Theory, Models, Algorithms and Applications, Metz, France, 8–10 July 2019
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