Concerning the shortcomings of unbalanced search， easy to fall into local optimum and weak comprehensive solution performance of Teaching-Learning-Based Optimization （TLBO） algorithm in dealing with optimization problems， an improved TLBO based on equilibrium optimization and Lévy flight strategy， namely ELMTLBO （Equilibrium-Lévy-Mutation TLBO）， was proposed. Firstly， an elite equilibrium guidance strategy was designed to improve the global optimization ability of the algorithm through the equilibrium guidance of multiple elite individuals in the population. Secondly， a strategy combining Lévy flight with adaptive weight was added after the learner phase of TLBO algorithm， and adaptive scaling was performed by the weight to the step size generated by Lévy flight， which improved the population's local optimization ability and enhanced the self-adaptability of individuals to complex environments. Finally， a mutation operator pool escape strategy was designed to improve the population diversity of the algorithm by the cooperative guidance of multiple mutation operators. To verify the effectiveness of the algorithm improvement， the comprehensive convergence performance of the ELMTLBO algorithm was compared with 7 state-of-the-art intelligent optimization algorithms such as the Dwarf Mongoose Optimization Algorithm （DMOA）， as well as the same type of algorithms such as Balanced TLBO （BTLBO） and standard TLBO on 15 international test functions. The statistical experiment results show that compared with advanced intelligent optimization algorithms and TLBO algorithm variants， ELMTLBO algorithm can effectively balance its search ability， not only solving both unimodal and multimodal problems， but also having significant optimization ability in complex multimodal problems. It can be seen that with the combined effect of different strategies， ELMTLBO algorithm has outstanding comprehensive optimization performance and stable global convergence performance. In addition， ELMTLBO algorithm was successfully applied to the Multiple Sequence Alignment （MSA） problem based on Hidden Markov Model （HMM）， and the high-quality aligned sequences obtained by this algorithm can be used in disease diagnosis， gene tracing and some other fields， which can provide good algorithmic support for the development of bioinformatics.

To improve the query efficiency of road routing algorithm in large-scale traffic network, a landmark-oriented algorithm based on A* algorithm was proposed. Select the most important vertexes and edges as landmarks during preprocessing, choose appropriate landmarks as the reference parameters and calculate in sections in point-to-point routing. The experimental results indicate that it has higher query efficiency and more reasonable results in long-distance road routing.

To reduce the estimation's complexity, an algorithm for traffic matrix estimation based on generalized linear inversion theory was proposed. For the purpose of improving the algorithm stability, a Singular Value Decomposition （SVD） decomposition method was used to find an optimum damping coefficient. Moreover, the historical average and link data were used to get the prior information so that the number of solutions can be reduced. Simulation results using Abilene network's actual data show that the proposed algorithm can guarantee good real-time capability while the computational accuracy can also be improved significantly.

In the preprocessing stage of fully automatic medical inspection pipelines， a test tube and liquid level detection algorithm based on improved YOLOv8 was proposed to address the issues of low recognition rate and low speed of test tube sizes and liquid levels in traditional detection methods. Firstly， a lightweight ADown module was employed to replace the Conv module in the backbone network for feature extraction and downsampling， thereby extracting more effective information while reducing the model size. Secondly， Bi-directional Feature Pyramid Network （BiFPN） was utilized for feature fusion， and more hierarchical feature information was integrated through bidirectional and skip connections. Additionally， Omni-dimensional Dynamic Convolution （ODConv） was introduced at the neck， and the C2f-ODConv module was designed to enhance feature extraction capabilities. Finally， the Inner-CIoU bounding box loss function was introduced to accelerate model convergence by auxiliary bounding boxes. Experimental results demonstrate that the proposed algorithm achieves improvements of 3.6， 4.8 and 5.0 percentage points in precision， recall and mean Average Precision （mAP）@50， and a decrease in computational cost （FLOPs） by 13.6% on a self-made dataset. It can be seen that the proposed model can realize accurate recognition of test tubes and liquid levels in real scenarios.