For the complex multimodal optimization problems, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm is easy to fall into local search and has low optima efficiency. In order to solve the problem, an improved TLBO algorithm based on K-means was proposed in this paper. It used the K-means to decide the population into small populations for reducing the population size and correspondingly improved the "teaching" and "learning" stages to improve the speed of global convergence. At the same time, the proposed algorithm added "mutation" operation to avoid the local optimum. In the experiments, seven unimodal and two multimodal optimization problems were optimized by the algorithm proposed in this paper. The optimization results were compared grenade explosion method and traditional TLBO algorithm. The experimental results show that the improved algorithm can quickly and efficiently find the globally optimal solution in both unimodal and multimodal functions and the improved algorithm is better than the traditional TLBO algorithm in the ability of searching the globally optimal solutions.

In order to solve the problem of class imbalance which the original degradation method causes in MIMLBoost algorithm, this paper introduced the importance of class into the original algorithm and an improved degradation method based on the category tag evaluating was proposed. First of all, the proposed method used a clustering algorithm to cluster all bags into groups. Each group could be treated as a concept in the multi-instance bag, and every class label could be quantified in each group. Then, the TF-IDF(Term Frequency-Inverse Document Frequency) algorithm was used to get the importance of each label in each group. Finally, for each group, the label whose importance was lowest in the group could be removed, because this label created many negative samples easily when the MIML (Multi-Instance Multi-Label) samples were transformed into multi-instance samples. The experimental results show that the new degradation method is effective, and the performance of improved algorithm is better than the original algorithm, especially in the terms of Hamming loss, coverage and ranking loss. This confirms that the new algorithm can reduce the error rate of classification and improve the precision of algorithm effectively.

In wireless video transmission system, the network bandwidth is often limited and changing, which leads to poor quality and instability of the video information in this process. Therefore, a rate control regulation was needed in the video codec. In order to make up for the deficiency of Average Bit-Rate (ABR) algorithm in X264, two methods were proposed in this article. According to the gap between actual output bits and target ones, one is a new compensation algorithm in the frame layer to adjust the Quantization Parameters (QP) of the current frame and the other is to rewrite the growth function of the buffer to control its excessive growth. These two methods have been evaluated with different target bits but the same video sequence, and with different video sequences but the same target bits, respectively. The results show that actual output bit rate is closer to the target one on condition that the average Peak Signal-to-Noise Ratio (PSNR) stays the same.