The performance of the Graph-based Semi-Supervised Learning (GSSL) method based on one graph mainly depends on a well-structured single graph and most algorithms based on multiple graphs are difficult to be applied while the data has only single view. Aiming at the issue, a Graph Transduction via Alternating Minimization method based on Multi-Graph (MG-GTAM) was proposed. Firstly, using different graph construction parameters, multiple graphs were constructed from data with one single view to represent data point relation. Secondly,the most confident unlabeled examples were chosen for pseudo label assignment through the integration of a plurality of map information and imposed higher weights to the most relevant graphs based on alternating optimization,which optimized agreement and smoothness of prediction function over multiple graphs. Finally, more accurate labels were given over the entire unlabeled examples by combining the predictions of all individual graphs. Compared with the classical algorithms of Local and Global Consistency (LGC), Gaussian Fields and Harmonic Functions (GFHF), Graph Transduction via Alternation Minimization (GTAM), Combined Graph Laplacian (CGL), the classification error rates of MG-GTAM decrease on data sets of COIL20 and NEC Animal. The experimental results show that the proposed method can efficiently represent data point relation with multiple graphs, and has lower classification error rate.

As standard Backtracking Search Optimization Algorithm (BSA) has the shortcoming of slow convergence, a new mutation scale factor based on Maxwell-Boltzmann distribution and a crossover strategy with greedy property were introduced to improve it. Maxwell-Boltzmann distribution was used to generate mutation scale factor, which could enhance search efficiency and convergence speed. Mutation population learning from outstanding individuals was adopted in less exchange-dimensional crossover strategy to add greedy property to crossover as well as fully ensure population diversity, which managed to avoid the problem that most existed algorithms easily trap into local minima when added greedy property. The simulation experiments were conducted on fifteen Benchmark functions. The results show that the improved algorithm has faster convergence speed and higher convergence precision, even in the high-dimensional multimodal functions, the improved algorithm's search results are nearly 14 orders of magnitude higher than those of original BSA after the same iterations, and its convergence precision can reach 10-10 or less.

The multi-coverage will appear with the sensors of Wireless Sensor Network (WSN) being randomly and high-densely distributed on the fields that will waste the energy of sensors and the entire network. Concerning this problem, a sleeping algorithm based Voronoi diagram was improved. Sleeping sensors were estimated and the energy of the whole network cost was reduced with calculating distance of sensors and their neighbors and distance of sensors and vertex of their Voronoi diagrams. The simulation results show that the improved sleeping algorithm can save energy of the whole network, and extend the lifetime of network.