When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils.

Aero-engine is known as the heart of an aircraft. Fatigue is one of the main causes of aero-engine failure, therefore, it is essential to predict the fatigue life in the aero-engine design process. Due to the uncertainty of influencing factors, it is necessary to further analyze the fatigue reliability. First, the fatigue life should be predicted on the basic of finite element analysis. The steps include parametric modeling, stress-strain analysis, load spectrum acquisition, and selection of fatigue life prediction model. Then, the reliability estimation of fatigue life should be employed, including the statistical analysis of influencing factors, reliability analysis method, and reliability estimation of fatigue life. Taking turbine blade and test probe of aero-engine as the research objects, the fatigue reliability analysis system is developed based on the ABAQUS-MATLAB platform. Statistical analysis shows that fatigue life approximately obeys lognormal distribution, and the distribution parameters estimated by MCS and Kriging are coincide, while Kriging only needs dozens of training samples. Under different reliability indexes, the design fatigue life error between Kriging and MCS is less than 1%, which meets the accuracy requirements and can effectively guide the fault detection and maintenance of aero-engine.