For solving the problem of multi-objective community detection in complex network, a Discrete Random Drift Particle Swarm Optimization (DRDPSO) algorithm was proposed. Firstly, by performing random coding operation on communities and using discretization operation for random drift optimization algorithm, the local network structure was improved and the global modularity value was gradually enhanced. Secondly, two objective functions, Kernel K-Means (KKM) and Ratio Cut (RC), were used to control the community size in the network and ameliorate the modularity resolution ratio. Finally, the Pareto non-inferior solution sets were updated step by step according to the multi-objective solving strategy, and the objective community structures satisfying the requirements were selected from the Pareto non-inferior solution sets. To verify the effectiveness of proposed algorithm, the comparison experiments of DRDPSO algorithm with other community detection algorithms were carried out on three generation networks with 10 different parameter configurations and three real networks. And the community detection results obtained by different algorithms were compared and analyzed by using two evaluation indicators of best community. Experimental results show that using DRDPSO algorithm for solving the multi-objective community detection problem in complex network, the probability of obtaining the highest community detection evaluation indexes (normalized mutual information and modularity) is more than 95%. The application of DRDPSO algorithm in real network can further improve the accuracy and robustness of network community division.

Aiming at the nonlinear design optimization problems with multiple constraints in the field of engineering shape design, an Adaptive Gaussian Quantum-behaved Particle Swarm Optimization (AG-QPSO) algorithm was proposed. By adjusting the Gaussian distribution adaptively, AG-QPSO algorithm was able to have strong global search ability at the initial stage of search process, and with the search process continued, the algorithm was able to have stronger local search ability, so as to meet the demands of the algorithm at different stages of the search process. In order to verify the effectiveness of the algorithm, 50 rounds of independent experiments were carried out on the two engineering constraint optimization problems: pressure vessel design and tension string design. The experimental results show that AG-QPSO algorithm achieves the average result of 5 890.931 5 and the optimal result of 5 885.332 8 on the pressure vessel design problem, and achieves the average result of 0.010 96 and the optimal result of 0.010 96 on the tension string design problem, which are better than the results of the existing algorithms such as the standard Particle Swarm Optimization (PSO) algorithm, Quantum Particle Swarm Optimization (QPSO) algorithm and Gaussian Quantum-behaved Particle Swarm Optimization (G-QPSO) algorithm. At the same time, the small variance of the results obtained by AG-QPSO algorithm indicates that the algorithm is very robust.

It is difficult to obtain the overall optimal solution by the traditional order batching and the picking path step-by-step optimization of picking operation in the logistics distribution center. In order to improve the efficiency of picking operation, a joint picking strategy based on nested genetic algorithm for order batching and path optimization was proposed. Firstly, the joint optimization model of order batching and picking path was established with the shortest total picking time as the objective function. Then, a nested genetic algorithm was designed to solve the model with the consideration of the complexity of double optimizations. The order batching result was continuously optimized in the outer layer, and the picking path was optimized in the inner layer according to the order batching result in the outer layer. Results of the examples show that, compared with the traditional strategies of order step-by-step optimization and step-by-step optimization in batches, the proposed strategy has reduced the picking time by 45.6% and 6% respectively, and the joint optimization model based on nested genetic algorithm results in shorter picking path and less picking time. To verify that the proposed algorithm has better performance on orders with different sizes, the simulation experiments were performed to the examples with 10, 20, 50 orders respectively. The results show that, with the increase of order quantity, the overall picking distance and time are further reduced, the decrease of picking time is risen from 6% to 7.2%.The joint optimization model of picking operation based on nested genetic algorithm and its solution algorithm can effectively solve the joint optimization problem of order batching and picking path, and provide the basis for the optimization of picking system in the distribution center.

The deep learning model increases the number of hidden layers, which makes the model have a good effect on speech recognition, image video classification and so on. However, to establish a model suitable for a specific object, a large number of data sets are required to train it for a long time to get the appropriate weights and biases. To resolve the above problems, a sub-health diagnosis method for rolling bearing was proposed based on depth autoencoder-relevance vector machine network model. Firstly, the bearing vibration signal was collected and transformed by Fourier transform and normalization. Secondly, the improved automatic encoder, named sparse edge noise reduction autoencoder, was designed, which combined the features of sparse automatic encoder and edge noise reduction automatic encoder. Then the depth autoencoder-relevance vector machine network model was designed, in which the supervised function was used to finely tune the parameters of each hidden layer, and it was trained by Relevance Vector Machine (RVM). Finally, the final classification results were obtained according to D-S (Dempster-Shafer) evidence fusion theory. The experimental results show that the proposed algorithm can effectively improve the recognition precision of the "sub-health" state of the rolling bearing and correct the error classification.

The performances and correctness of system are affected by the state transition real-time process of the cyber-physical system. In order to solve the problem, aiming at the state transition process of actor's output-event driven system, a new scheduling strategy of value evaluation for output-event of actor named Value Evaluation-Information Entropy and Quality of Data (VE-IE&QoD) was proposed. Firstly, the real-time performance of event was expressed through the super dense time model. The self-information of the output-event, the information entropy of the actor and the quality of data were defined as the function indexes of value evaluation. Then, the value evaluation mission was executed for the process of the actor in performing task and it was considered about suitably increasing the weighting coefficient for parametric equation. Finally, the discrete event models which contain the proposed VE-IE&QoD scheduling strategy, the traditional Earliest Deadline First (EDF) scheduling algorithm and Information Entropy ^* (IE ^*) scheduling strategy were built by Ptolemy Ⅱ platform. The operation situation of different algorithm models was analyzed, the change of value evaluation and execution time of different algorithm models were compared. The experimental results show that, the VE-IE&QoD scheduling strategy can reduce the system average execution time, improve the memory usage efficiency and task value evaluation. The proposed VE-IE&QoD scheduling strategy can improve the system performance and correctness to some extent.

According to the problem that better dimensions information of particles will loss in Quantum-behaved Particle Swarm Optimization (QPSO) algorithm when solving multi-dimensions problems, a strategy with crossover operator was introduced and the quality of solutions and the performance of algorithm would be improved. Firstly, the whole update and evaluation strategy on solutions in algorithm was analyzed and the better dimensions information of particles would loss because of the mutual interference between dimensions. Secondly, when the evolution was executed dimension by dimension, the algorithm complexity would increase exponentially. Finally, multi-crossover method was employed to increase the retaining probability of excellent dimension information. The comparison and analysis results of the proposed method, with linearly decreased coefficient control method and non-linearly decreased coefficient control method on 12 CEC2005 benchmark functions were given. The simulation results show the modified algorithm can greatly improve the QPSO performance compared with the basic QPSO in 10 functions and also get better performance in 7 functions compared with the other two QPSO variants. Therefore, the proposed method can improve the performance of QPSO effectively.

In order to reveal the evolution rules of supply chain network with the core of manufacturers, a kind of five-level local world network model was put forward. This model used the BA model and the multi-local world theory as the foundation, combined with the reality of network node generation and exit mechanism. First of all, the intrinsic characteristics and evolution mechanism of network were studied. Secondly, the topology structure and evolution rules of the network were analyzed, and the simulation model was established. Finally, the changes of network characteristic parameters were simulated and analyzed in different time step and different critical conditions, including nodes number, clustering coefficient and degree distribution, then the evolution law of the network was derived. The simulation results show that the supply chain network with the core of manufacturers has the characteristics of scale-free and high concentration. With the increase of time and the growth rate of the network nodes, the degree distribution of overall network approaches to the power-law distribution with the exponent three. The degree distribution of the network at all levels is different, sub-tier suppliers and retailers obey power-law distribution, suppliers and distributors obey exponential distribution, manufacturers generally obey the Poisson distribution.

The present HowNet-based vocabulary semantic similarity calculation method fails to give due attention to the linear feature of conceptual description in knowledge database mark-up language. To resolve this shortcoming, an improved vocabulary semantic similarity calculation method was proposed. Firstly, fully considering the linear relationship between the sememes in the conceptual description formula, a position-related weight distribution strategy was proposed. Then concept similarity was calculated by combining the strategy above with bigraph maximum weight matching. The experimental results show that, compared with the contrast method, the F-measure of text clustering using improved method increases by 5% on average, thus verifying the rationality and validity of the improved method.

Concerning the MPC8247 target system based on PowerPC, the device tree was discussed and an embedded Linux system was developed, including the transplant and deployment of U-Boot, Linux kernel, Device Tree Blob (DTB) and Ramdisk file system. The actual operation of the system shows that the device tree file is correct, and the system design is rational and efficient.

To eliminate the mosaic and "bell" effects due to cumulative errors during large object image inpainting, the Markov Random Fields (MRF) exemplar inpainting based on dual-tree complex wavelet domain was proposed. The image was converted to complex-frequency domain by Dual-Tree Complex Wavelet Transform (DTCWT) and the exemplar inpainting order was computed by rational confidence and data item, the unknown region was inpainted based on multiscale and multiband. The inpainted images were reconstructed by dual-tree complex wavelet inverse transform. The experimental results show that compared with classical discrete wavelet methods, the mosaic and "bell" effects can be avoided and the more favorable textural and structural information can be preserved.