Surrogate-guided multi-objective optimization (SGMOO) using an efficient online sampling strategy

2021 ◽  
Vol 220 ◽  
pp. 106919
Author(s):  
Huachao Dong ◽  
Jinglu Li ◽  
Peng Wang ◽  
Baowei Song ◽  
Xinkai Yu
PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254839
Author(s):  
Qingyang Zhang ◽  
Shouyong Jiang ◽  
Shengxiang Yang ◽  
Hui Song

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.


Author(s):  
Zhaoyun Song ◽  
Bo Liu ◽  
Hao Cheng ◽  
Xiaochen Mao

To improve the flow characteristics of tandem cascades on design and off design incidence angle and increase the stable operation range, a multi-objective optimization methodology based on CO-kriging and parallel multi-point sampling strategy is presented to realize multi-objective optimization of tandem cascades. Co-kriging model created by a greater quantity of low-fidelity samples coupled with a small amount of high-fidelity samples is introduced to reduce the compute cost of multi-objective optimization problems. The prediction performances of Co-kriging are much better than those of Kriging based on two numerical examples. The multi-point sampling strategy based on the fuzzy c-means clustering method can realize a good balance between exploitation known regions and exploration unknown regions for selecting new samples to update the Co-kriging. And the multi-objective optimization methodology can obtain the approximate Pareto frontier at a less compute cost and was validated by applying it to achieve the multi-objective optimization of a high-turning tandem cascade. After optimization, for the optimal tandem cascade, the static pressure ratio is higher and the total pressure loss coefficient is smaller at all incidence angle conditions. At inlet Mach number of 0.7, when incidence angle is −6° and 3°, the total pressure loss coefficient is respectively decreased by 21% and 35%. Tandem cascades with a high PP (about 0.92) and a negative KBB (about −6°) can realize good flow performances on design and off design incidence angle. And a large TR can improve the flow characteristics of tandem cascades on design and off design incidence angle and increase the stable operation range.


2017 ◽  
Vol 10 (5) ◽  
pp. 371
Author(s):  
Arakil Chentoufi ◽  
Abdelhakim El Fatmi ◽  
Molay Ali Bekri ◽  
Said Benhlima ◽  
Mohamed Sabbane

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