RESUMEN
In this paper, two novel algorithms are designed for solving biobjective optimization engineering problems. In order to obtain the optimal solutions of the biobjective optimization problems in a fast and accurate manner, the algorithms, which have combined Newton's method with Neumann series expansion as well as the weighted sum method, are applied to deal with two objectives, and the Pareto optimal front is achieved through adjusting weighted factors. Theoretical analysis and numerical examples demonstrate the validity and effectiveness of the proposed algorithms. Moreover, an effective biobjective optimization strategy, which is based upon the two algorithms and the surrogate model method, is developed for engineering problems. The effectiveness of the optimization strategy is proved by its application to the optimal design of the dummy head structure in the car crash experiments.
RESUMEN
Many materials in nature exhibit excellent mechanical properties. In this study, we evaluated the bionic bumper structure models by using nonlinear finite element (FE) simulations for their crashworthiness under full-size impact loading. The structure contained the structural characteristics of cattail and bamboo. The results indicated that the bionic design enhances the specific energy absorption (SEA) of the bumper. The numerical results showed that the bionic cross-beam and bionic box of the bionic bumper have a significant effect on the crashworthiness of the structure. The crush deformation of bionic cross-beam and box bumper model was reduced by 33.33%, and the total weight was reduced by 44.44%. As the energy absorption capacity under lateral impact, the bionic design can be used in the future bumper body.
RESUMEN
Research on a method for fast selecting feature wavelengths from the nitrogen spectral information is necessary, which can determine the nitrogen content of crops. Based on the uniformity of uniform design, the present paper proposed an improved particle swarm optimization (PSO) method. The method can choose the initial particle swarm uniformly and describe the optimization space well by fewer sample points, which is helpful to avoiding the local optimum and accelerate the convergence. Then, the method was applied to fast select the nitrogen spectral wavelengths of soybean, cotton and maize. Calibration models based on the partial least square (PLS) method and selected wavelengths were constructed. The results illustrate that compared with the original wavelengths, the number of selected wavelengths decreases about 93%, which means the computation is simplified. Also, the precision of PLS prediction mode based on the selected wavelengths improves by 34% at least, and the prediction ability of calibration model increases greatly. Therefore, the proposed method is both correct and effective.
