Since the deterministic algorithms are difficult to solve the Discounted {0-1} Knapsack Problem (D{0-1}KP) with large-scale and wide data range, a Mutated Double codes Binary Bat Algorithm (MDBBA) was proposed. Firstly, the coding problem of D{0-1} KP was solved by double coding. Secondly, the Greedy Repair and Optimization Algorithm (GROA) was applied to the individual fitness calculation of bats, and the algorithm was quickly and effectively solved. Then, the mutation strategy in Differential Evolution (DE) was selected to improve the global optimization ability. Finally, Lévy flight was carried out by the bat individual according to certain probability to enhance the ability of the algorithm to explore and jump out of local extrema. Simulation was tested on four large-scale instances. The result shows that MDBBA is very suitable for solving large-scale D {0-1} KP, which has better optimal value and mean value than FirEGA (First Genetic Algorithm) algorithm and Double Binary Bat Algorithm (DBBA), and MDBBA converges significantly faster than DBBA.

In order to solve the problem that large amounts of supervised information was needed to achieve satisfactory performance, owing to the implementation of the semi-supervised community detection methods based on Non-negative Matrix Factorization (NMF) which selected prior information randomly, an Active Link Selection algorithm for semi-supervised community detection based on Graph regularization NMF (ALS_GNMF) was proposed. Firstly, in the iteration framework, the most uncertain and informative links were selected actively as prior information links. Secondly, the must-link constraints of these links, which generated the prior matrix, were added to enhance the connections in a certain community. At the same time, the cannot-link constraints were added, which modified the adjacency matrix, to weaken the connections between communities. Finally, the prior matrix was used as a graph regularization term to incorporate into the optimization objective function of NMF. And combining with network topology information, higher community discovery accuracy and robustness were achieved with less prior information. At the same prior ratio on both synthetic and real networks, experimental results demonstrate that the ALS_GNMF algorithm significantly outperformes the existing semi-supervised NMF algorithms in terms of efficiency, and it is stable especially on networks with unclear structure.