To solve the virtual machine deployment problem in Infrastructure as a Service (IaaS) of cloud computing, a virtual machine deployment strategy based on Particle Swarm Optimization (PSO) algorithm was proposed. Since the PSO algorithm has weaknesses of having a slow convergence speed and falling into local optimum easily when dealing with large-scale and complex problems like virtual machine deployment, firstly, a Multiple-population Gaussian Learning Particle Swarm Optimization (MGL-PSO) algorithm was proposed, with using the model of multiple population evolution to accelerate the algorithm convergence, as well as adding Gaussian learning strategy to avoid local optimum. Then according to the deployment model, with using Round Robin (RR) algorithm to initialize the MGL-PSO, a virtual machine deployment strategy aiming to load balancing was proposed. Through the simulation experiment in CloudSim, it validates that MGL-PSO has a higher convergence speed and load imbalance degree is reduced by 13% compared with PSO algorithm. In the two experimental situations, compared with the Opportunistic Load Balancing (OLB) algorithm, the load imbalance degrees of the proposed algorithm decrease by 25% and 15% respectively, and compared with the Greedy Algorithm (GA) the load imbalance degrees decrease by 19% and 7% respectively.

Conventional approaches for Common Weights (CW) generation in Data Envelopment Analysis (DEA) are either non-linear or scale-relevant. To solve this problem, according to the demand of military training performance evaluation, a new method was proposed to generate CW in DEA. The new method took DEA efficient units as the basis of calculation. Firstly, training data were normalized, and then multi-objective programing was employed for CW generation, which can lead to a fairer and more reasonable ranking of performances. The proposed method is not only linear, but also scale-irrelevant. Lastly, a military application illustrates that the proposed method is scientific and effective.

In order to solve the difficulty of selecting cluster centers and weakness of density analysis generalization, a novel clustering method was proposed. The method completed clustering by density and distance analysis based on genetic algorithm, which computed density with exponential method to reduce the impact of parameters and adopted genetic algorithm to search optimum threshold values. It introduced a penalty factor to overcome the excursion of search region for accelerating convergence. Numerical experiments on both artificial and UCI data sets show that compared with K-means, fast search clustering and Max_Min_SD, the proposed algorithm can achieve better or comparable performance on Rand index, accuracy, precision and recall.

Quantization step is a key parameter in quantization modulation method, but now there is no theoretical method to decide its value. To solve this problem, a quantitative estimation method of quantization step based on Peak Signal-to-Noise Ratio (PSNR) was proposed. In this method, dither quantization modulation method was chosen as research object, and wavelet coefficients were regarded as quantization coefficients. According to the distribution of quantization error, it firstly gave an estimating quantization error method based on quantization step. Then it deduced the quantitative relationship equation of quantization step, watermark sequence length and PSNR on the basis of some properties of wavelet transform. The experimental results show that PSNR values calculated through quantitative equation are in good agreement with those obtained from experiments when the values of their quantization step are the same. It demonstrates that the deduced quantitative relationship equations are accurate.

The exiting model representation cannot meet the requirements of multistage modeling, so model share, reuse and management can hardly achieve in multistage modeling process. Therefore, a hierarchical model management framework based on universal relation model presentation was presented. Firstly, the requirements of model representation in model management and the limitations of exiting model representation were analyzed, then a model representation based on universal relation was investigated to set the mapping relation between layers of conceptual model and mathematical model, and the integrative model representation and the hierarchical model management framework including conceptual model, mathematical model and physical model were set up. At last, the logic of modelbase in management framework was designed and the physical model generation based on universal relation was investigated. The model of different modeling process could be managed uniformly based on an integrative model representation.