Arthropod object detection in natural environment has characteristics of complex object background， large scale difference， and dense objects，resulting in poor object detection accuracy and precision. Therefore， an arthropod object detection method was proposed based on the improved Faster RCNN model， namely AROD RCNN （ARthropod Object Detection RCNN）. Firstly， a Supervised Parallel mechanism with Spatial and Channel ATtention modules （SPSCAT） was designed to improve the accuracy of arthropod object detection in the environment with complex background. Then， the second-generation deformable convolution was introduced to reconstruct the convolutional layer with C1~C5 blocks in ResNet50， and the Feature Pyramid Network （FPN） was adopted to perform feature fusion on the C2~C6 blocks in ResNet50 to solve the problem that large difference in object scale affected detection accuracy. Finally， the Dense Local Regression （DLR） method was used to improve the regression stage， thereby improving the accuracy of the model regression. Experimental results show that on ArTaxOr （Arthropod Taxonomy Orders Object Detection） dataset， the proposed method has the mean Average Precision （mAP） of 0.717， which is 0.453 higher than that of the original model， and has the recall reached 0.787. It can be seen that the proposed method can effectively solve the problems of object occlusion and complex background， and performs well in the detection of dense arthropod objects and small arthropod objects.

The single mechanism based Grey Wolf Optimizer (GWO) is easy to fall into local optimum and has slow convergence speed. In order to solve the problems, an Improved Grey Wolf Optimization (IGWO) was proposed to solve the actual location selection problem of railway logistics distribution center. Firstly, based on the basic GWO, the theory of good point set was introduced to initialize the population, which improved the diversity of the initial population. Then, the D-value Elimination Strategy (DES) was used to increase the global optimization ability, so as to achieve an efficient optimization mode. The simulation results show that, compared with the standard GWO, IGWO has the fitness value increased by 3%, and the accuracy of the optimal value increased by up to 7 units in 10 test functions. Compared with Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and Genetic Algorithm (GA), IGWO has the location selection speed increased by 39.6%, 46.5% and 65.9% respectively, and the location selection velocity is significantly improved. The proposed algorithm can be used for railway logistics center location selection.

In order to solve the problem of feature information loss caused by the introduction of a large number of pooling layers in traditional convolutional neural networks, based on the feature of Capsule Network (CapsNet)——using vector neurons to save feature space information, a network model 3DSPNCapsNet (3D Small Pooling No dense Capsule Network) was proposed for recognizing 3D models. Using the new network structure, more representative features were extracted while the model complexity was reduced. And based on Dynamic Routing (DR) algorithm, Dynamic Routing-based algorithm with Length information (DRL) algorithm was proposed to optimize the iterative calculation process of capsule weights. Experimental results on ModelNet10 show that compared with 3DCapsNet (3D Capsule Network) and VoxNet, the proposed network achieves better recognition results, and has the average recognition accuracy on the original test set reached 95%. At the same time, the recognition ability of the network for the rotation 3D models was verified. After the rotation training set is appropriately extended, the average recognition rate of the proposed network for rotation models of different angles reaches 81%. The experimental results show that 3DSPNCapsNet has a good ability to recognize 3D models and their rotations.

To solve the sparse and cold start problems of recommendation system, a Trust Regularization Euclidean Embedding (TREE) algorithm by fusing trust information was proposed. Firstly, the Euclidean embedding model was employed to embed the user and project in the unified low-dimensional space. Secondly, to measure the trust information, both the project participation degree and user common scoring factor were brought into the user similarity calculation formula. Finally, a regularization term of social trust relationship was added to the Euclidean embedding model, and trust users with different preferences were used to constrain the location vectors of users and generate the recommendation results. In the experiments, the proposed TREE algorithm was compared with the Probabilistic Matrix Factorization (PMF), Social Regularization (SoReg), Social Matrix Factorization (SocialMF) and Recommend with Social Trust Ensemble (RSTE) algorithms. When dimensions are 5 and 10, TREE algorithm has the Root Mean Squared Error (RMSE) decreased by 1.60% and 5.03% respectively compared with the optimal algorithm RSTE on the dataset Filmtrust.While on the dataset Epinions, the RMSE of TREE algorithm was respectively 1.12% and 1.29% lower than that of the optimal algorithm SocialMF. Experimental results show that TREE algorithm further alleviate the sparse and cold start problems and improves the accuracy of scoring prediction.

The way of selecting evolutionary parameters is vital for the optimal performance of the Quantum-inspired Evolutionary Algorithm (QEA). However, in conventional QEA, all individuals employ the same evolutionary parameters to complete update without considering the individual difference of the population, thus the drawbacks including slow convergence speed and being easy to fall into local optimal solution are exposed in computing combination optimization problem. To address those problems, an adaptive evolutionary mechanism was employed to adjust the rotation angle step and the quantum mutation probability in the quantum evolutionary algorithm. In the algorithm, the evolutionary parameters in each individual and each evolution generation were determined by the individual fitness to ensure that as many evolutionary individuals as possible could evolve to the optimal solution direction. In addition, the adaptive-evolution-based evolutionary algorithm needs to evaluate the fitness of each individual, which leads to a longer operation time. To solve this problem, the proposed adaptive quantum-inspired evolutionary algorithm was parallel implemented in different universe to improve the execution efficiency. The proposed algorithms were tested by searching the optimal solutions of three multimodal functions and solving knapsack problem. The experimental results show that, compared with conventional QEA, the proposed algorithms can achieve better performances in convergence speed and searching the global optimal solution.