Replacing expensive objective function evaluations with computationally inexpensive surrogate models to assist evolutionary algorithms in solving expensive black-box multi-objective optimization problems has garnered significant attention in recent years. Model accuracy plays a critical role in surrogate-assisted Multi-Objective Evolutionary Algorithms （MOEAs）； particularly when dealing with numerous objective functions， inaccurate models may misguide the search direction. However， due to the high cost of objective function evaluation， obtaining sufficient training samples to build high-quality surrogate models remains challenging. To address this issue， a Two-stage Infill Sampling-based Semi-supervised Expensive Multi-objective Optimization Algorithm （TISS-EMOA） was proposed. Semi-supervised techniques were introduced to augment the training dataset by selecting partial unlabeled data， thereby improving model accuracy. Simultaneously， a two-stage infill sampling criterion was introduced to acquire high-quality solutions for expensive multi-objective optimization problems under limited evaluation budgets. To validate the effectiveness of TISS-EMOA， experiments were conducted on the DTLZ1 - DTLZ7 benchmark problems and a real-world vehicle frontal structure optimization design. Compared with five State-Of-The-Art （SOTA） surrogate-assisted multi-objective evolutionary algorithms， TISS-EMOA achieves 25， 28， 28，24， 23 optimal or equal Modified Inverted Generational Distance （IGD⁺） results in 28 benchmark problems.

To address the problem that the existing algorithm uses synchronous manual optimization of deep learning networks， and ignores the negative information of network learning， which leads to a large number of redundant parameters or even overfitting， thereby affecting the counting accuracy， a parameter asynchronous updating algorithm based on Multi-column Convolutional Neural Network （MCNN） was proposed. Firstly， a single frame image was input to the network， and after three columns of convolutions to extracting features with different scales respectively， the correlation of every two columns of feature maps was learned through the mutual information between columns. Then， the parameters of each column were updated asynchronously according to the optimized mutual information and the updated loss function until the algorithm converges. Finally， the dynamic Kalman filtering was used to deeply fuse the output density maps output by the columns， and all pixels in the fused density map were summed up to obtain the total number of people in the image. Experimental results show that on the UCSD （University of California San Diego） dataset， the Mean Absolute Error （MAE） of the proposed algorithm is 1.1% less than that of ic-CNN+McML （iterative crowd counting Convolution Neural Network Multi-column Multi-task Learning） with the best MAE performance on the dataset， and the Mean Square Error （MSE） of the proposed algorithm is 4.3% less than that of Contextual Pyramid Convolution Neural Network （CP-CNN） with the best MSE performance on the dataset； on the ShanghaiTech Part_A dataset， the MAE of the proposed algorithm is reduced by 1.7% compared to that of ic-CNN+McML with the best MAE performance on the dataset， and the MSE of the proposed algorithm is reduced by 3.2% compared to that of ACSCP （Adversarial Cross-Scale Consistency Pursuit）with the best MSE performance on the dataset； on the ShanghaiTech Part_B dataset， the proposed algorithm has the MAE and MSE reduced by 18.3% and 35.2% respectively compared to ic-CNN+McML with the best MAE and MSE performances on the dataset； on the UCF_CC_50 （University of Central Florida Crowd Counting） dataset， the proposed algorithm has the MAE and MSE reduced by 1.9% and 9.8% respectively compared to ic-CNN+McML with the best MAE and MSE performances on the dataset. The above shows that this algorithm can effectively improve the accuracy and robustness of crowd counting， and allows the input image to have any size or resolution， and can adapt to the large-scale transformation of the detected target.

An unsupervised extraction method is proposed in order to extract bead-like particles regions of fly ash from scanning electron microscope image, which is based on region growing with gray similarity bounded by gradient and shape. The process is automatic, including seeds selecting , regions growing and shape distinguishing. The experimental error is measured by the acreage probability of missing segmentation and false segmentation. The minimum error rate of the experimental results is 6.8%, and the average error rate is 8%. The time of extraction from 60 SEM images is within 10 minutes. The method is effective for the content estimate of fly ash in the material.

As most of the face recognition techniques will suffer serious performance drop when there is only one training sample per person, a face recognition algorithm based on sample augment methods and improved Two Dimensional Principal Component Analysis (2DPCA) was proposed. By analyzing the advantage and disadvantage of various sample augment methods, some of them were combined to synthesize virtual samples in order to make full use of the single training image. Improved 2DPCA was chosen to extract the feature of the synthetic virtual samples. The training samples were divided into sub-blocks and then the covariance matrix was constructed by these sub-blocks which were normalized by the within-class average value in each sub-block. The experimental results on ORL and Yale face database indicate that the performance of the proposed algorithm is better than those of general 2DPCA and the method only using sample augment.

This paper proposed a new method of face recognition, which used DNA algorithm mixed with Singular Value Decomposition (SVD). It aimed to quickly reduce the recognition targets of large scale face database, and make the next recognition process use regular methods possible. The experiment was carried out on standard ORL face database. The result indicates this method avails and DNA algorithm realizes its application on face recognition.