Image dehazing algorithms based on Transformer achieve good dehazing effects， but there are problems such as large number of network parameters and low dehazing speed. In order to prune redundant parts of the dehazing network directionally and shorten dehazing time without affecting dehazing quality， a Transformer image dehazing method based on component collaborative optimization pruning， CCOP-IDT （Component Collaborative Optimization Pruning Image Dehazing Transformer）， was proposed. Firstly， a 5-level Transformer was used to construct a pre-training model of dehazing network. Then， the network pruning was modeled as an optimization problem， Fisher information was used to evaluate the importance of weight parameters， and Hessian matrix was used to measure the joint influence of pruning components on network output， so as to establish a collaborative optimization method for multiple pruning components. Finally， an evolutionary algorithm was employed to solve the optimal pruning rate sequence， so as to obtain the optimal sub-network of the pre-trained model. Experimental results show that after pruning， the number of network parameters is controlled to 0.476×10⁶， which is reduced by 28.8% compared with that before pruning， and the dehazing time is shortened by 25.0%. On the synthetic hazy dataset RESIDE-6K， the proposed method has the Peak Signal-to-Noise Ratio （PSNR） reached 29.60 dB， and the Structural SIMilarity （SSIM） reached 0.968 7， which are reduced by only 1.63% and 0.46% compared with those before pruning， respectively. It can be seen that in terms of both quantitative and qualitative evaluation， the proposed method performs well with great reduction of the model parameters and improvement of the image dehazing speed while maintaining the quantitative indices and subjective perception basically.

In view of the wide application of time series data in various fields， the mining and representation of identifiable features of the data is crucial. Due to the influence of the data acquisition environment and acquisition equipment， time series data in many application fields are characterized by high noise， which puts forward high requirements for the robustness of data representation methods. Therefore， a Robust Shapelet representation method for Time series （TRS） was proposed， which adopts the feature extraction method of Key-Shapelet （KS）， retains the interpretability while reducing the influence of noise， and represents the time series by position distance measurement， thereby improving the robustness of the whole method. Experimental results on noise-disturbed time series data show that the features extracted by TRS are significantly better than those of the existing methods in classification， and the average accuracy of TRS is 2.1 percentage points higher than that of the deep learning model — Adversarial Dynamic Shapelet Network （ADSN）， which also extracts features based on Shapelets. It can be seen that the feature set extracted by TRS is more representative and robust.

Aiming to solve the problems of large-scale resources， complex constraints， and difficult cooperation of multi-timescale decision-making in the crude oil scheduling process， a Multi-Timescale Cooperation Evolutionary Algorithm （MTCEA） was proposed. Firstly， a large-scale multi-timescale crude oil scheduling optimization model was established according to the scale structure and actual demand of oil refining enterprises， which consists of a resource-oriented medium- and long-term scheduling model and an operation-oriented short-term scheduling model， and achieves a reasonable allocation of crude oil resources through employing a dynamic grouping strategy of crude oil resources to satisfy the requirements of different scheduling scales， multi-timescale characteristics， and fine production. Secondly， to promote the integration of scheduling decisions at different time scales， an evolutionary algorithm based on multi-timescale cooperation was designed and solved by constructing subproblems for the continuous decision variables in scheduling models at different time scales to achieve cooperation optimization between scheduling decisions at different time scales. Finally， MTCEA was verified in three practical industrial cases. Compared with three representative large-scale evolutionary optimization algorithms （i.e.， Competitive Swarm Optimizer （CSO）， Self-adaptive Differential Evolution with Modified Multi-Trajectory Search （SaDE-MMTS）， and Mixture Model-based Evolution Strategy （MMES）） and three high-performance Mixed Integer Non-Linear Programming （MINLP） mathematical solvers （ANTIGONE （Algorithms for coNTinuous/Integer Global Optimization of Nonlinear Equations）， SCIP （Solving Constraint Integer Programs）， and SHOT （Supporting Hyperplane Optimization Toolkit））， the results show that the metrics of the solution optimality and stability of MTCEA are improved by more than 30% and 25%， respectively. These significant performance improvements demonstrate the practical application value and advantages of MTCEA in large-scale multi-timescale crude oil scheduling decisions.

Constructing the transition probability matrix for outlier detection by using traditional graph-based methods requires the use of the overall distribution of the data， and the local information of the data is easily ignored， resulting in the problem of low detection accuracy， and using the local information of the data may lead to “suspended link” problem. Aiming at these problems， an Outlier Detection algorithm based on Hologram Stationary Distribution Factor （HSDFOD） was proposed. Firstly， a local information graph was constructed by adaptively obtaining the set of neighbors of each data point through the similarity matrix. Then， a global information graph was constructed by the minimum spanning tree. Finally， the local information graph and the global information graph were integrated into a hologram to construct a transition probability matrix for Markov random walk， and the outliers were detected through the generated stationary distribution. On the synthetic datasets A1 to A4， HDFSOD has higher precision than SOD （Outlier Detection in axis-parallel Subspaces of high dimensional data）， SUOD （accelerating large-Scale Unsupervised heterogeneous Outlier Detection）， IForest （Isolation Forest） and HBOS （Histogram-Based Outlier Score）； and AUC （Area Under Curve） also better than the four comparison algorithms generally. On the real datasets， the precision of HSDFOD is higher than 80%， and the AUC of HSDFOD is higher than those of SOD， SUOD， IForest and HBOS. It can be seen that the proposed algorithm has a good application prospect in outlier detection.

Aiming at the complexity of port container throughput data， a short-term hybrid prediction model of container throughput based on Optimal Variational Mode Decomposition （OVMD） and Kernel Extreme Learning Machine （KELM） was proposed. Firstly， the outliers were removed by Hampel Identifier （HI） from the original time series， and the preprocessed series was decomposed into several sub-modes with obvious characteristics by OVMD. Then， in order to improve the prediction efficiency， the decomposed sub-modes were divided into three categories according to the values of Sample Entropy （SE）： high frequency low amplitude， medium frequency medium amplitude and low frequency high amplitude. At the same time， the wavelet， Gauss and linear kernel functions carried in KELM were used to capture the trends of sub-modes with different characteristics. Finally， the final prediction result was obtained by linearly adding the prediction results of all sub- modes together. Taking the monthly container throughput data at Shenzhen Port as a sample for empirical research， the proposed model has the Mean Absolute Error （MAE） of 0.914?9， the Mean Absolute Percentage Error （MAPE） of 0.199%， the Root Mean Square Error （RMSE） of 7.886?0 and the coefficient of determination （R²） of 0.994?4. Compared with four comparison models， the proposed model has advantages in prediction accuracy and efficiency. At the same time， it overcomes the mode mixing problem in traditional Complementary Ensemble Empirical Mode Decomposition （CEEMD） and Ensemble Empirical Mode Decomposition （EEMD） as well as overfitting defect in Extreme Learning Machine （ELM）， and has practical application potential.

In order to solve the problems of large amount of network computation and difficulty in distinguishing falling-like behaviors in fall detection algorithms， a fall detection algorithm based on joint point features was proposed. Firstly， based on the current advanced CenterNet algorithm， a Depthwise Separable Convolution-CenterNet （DSC-CenterNet） joint point detection algorithm was proposed to accurately detect human joint points and obtain joint point coordinates while reducing the amount of backbone network computation. Then， based on the joint point coordinates and prior knowledge of the human body， the spatial and temporal features expressing the fall behavior were extracted as the joint point features. Finally， the joint point feature vector was input into the fully connected layer and processed by Sigmoid classifier to output two categories： fall or non-fall， thereby achieving the fall detection of human targets. Experimental results on UR Fall Detection dataset show that the proposed algorithm has the average accuracy of fall detection under different state changes reached 98.00%， the accuracy of distinguishing falling-like behaviors reached 98.22% and the fall detection speed of 18.6 frame/s. Compared with the algorithm of the original CenterNet combining with joint point features， the algorithm of DSC-CenterNet combining with joint point features has the average detection accuracy increased by 22.37%. The improved speed can effectively meet the realtime requirement of the human fall detection tasks under surveillance video. This algorithm can effectively increase fall detection speed and accurately detect the fall state of human body， which further verifies the feasibility and efficiency of fall detection algorithm based on joint point features in the video fall behavior analysis.

Image registration is a well researched topic of computer vision. To deal with matching efficiency, repetitive pattern matching and affine invariant matching better, two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform (ASIFT) algorithm were presented. The feature extraction of matching frame was developed to improve the matching efficiency of the ASIFT algorithm. The second increased the accuracy of matching and the adaptive capacity of repetitive patterns through the use of improved matching algorithm by combining Optimized Random Sample Consensus (ORSA) with Random Sample Consensus (RANSAC) algorithm based on geometric linear constraint model with homography matrix. The experimental results show that the proposed method is able to well match highly repetitive patterns and has smaller calculation, faster speed and higher accuracy as well.

Optimum Multi-user Detection (OMD) technique can achieve the theoretical minimum error probability, but it has been proven to be a Non-deterministic Polynomial (NP) problem. As a new swarm intelligence algorithm, Artificial Bee Colony (ABC) algorithm has been widely used in various optimization problems. However, the traditional Binary Artificial Bee Colony (BABC) algorithm has the shortcomings of slower convergence speed and falling into local optimum easily. Concerning the shortcomings, an improved binary artificial bee colony algorithm was proposed and used for optimum multi-user detection. The initialization process was simplified. The one-dimensional-reversal neighborhood search strategy was adopted. Compared with optimum multi-user detection, the computation complexity of the improved algorithm declines obviously. The simulation results show that the proposed scheme has significant performance improvement over the conventional detection in anti-multiple access interference and near-far resistance.