Aiming at the problem that the existing differential privacy high-dimensional data publishing methods are difficult to take into account both the complex attribute correlation between data and computational cost, a differential privacy high-dimensional data publishing method based on clustering analysis technology, namely PrivBC, was proposed. Firstly, the attribute clustering method was designed based on the K-means++, the maximum information coefficient was introduced to quantify the correlation between the attributes, and the data attributes with high correlation were clustered. Secondly, for each data subset obtained by the clustering, the correlation matrix was calculated to reduce the candidate space of attribute pairs, and the Bayesian network satisfying differential privacy was constructed. Finally, each attribute was sampled according to the Bayesian networks, and a new private dataset was synthesized for publishing. Compared with PrivBayes method, PrivBC method had the misclassification rate and running time reduced by 12.6% and 30.2% averagely and respectively. Experimental results show that the proposed method can significantly improve the computational efficiency with ensuring the data availability, and provides a new idea for the private publishing of high-dimensional big data.

The Self-Organized Migrating Algorithm (SOMA) only can solve the single task, and the "implicit parallelism" of SOMA is not fully exploited. Aiming at the shortcomings, a new Self-Organized Migrating Algorithm for Multi-task optimization with Information Filtering (SOMAMIF) was proposed to solve multiple tasks concurrently. Firstly, the multi-task uniform search space was constructed, and the subpopulations were set according to the number of tasks. Secondly, the current optimal fitness of each subpopulation was judged, and the information transfer need was generated when the evolution of a task stagnated in a successive generations. Thirdly, the useful information was chosen from the remaining subpopulations and the useless information was filtered according to a probability, so as to ensure the positive transfer and readjust the population structure at the same time. Finally, the time complexity and space complexity of SOMAMIF were analyzed. Experimental results show that, SOMAMIF converges rapidly to the global optimal solution 0 when solving multiple high-dimensional function problems simultaneously; compared with those of the original datasets, the average classification accuracies obtained on two datasets by SOMAMIF combing with the fractal technology to extract the key home returning constraints from college students with different census register increase by 0.348 66 percentage points and 0.598 57 percentage points respectively.

Focusing on the task allocation problem in spatial crowdsourcing, with the consideration of the influence of the spatial crowdsourcing workers' service quality on the allocation results, a task allocation strategy with the quality evaluation of worker's service was proposed. Firstly, in each spatio-temporal environment, the evaluation element of spatial crowdsourcing workers was added to establish a multi-objective model that fully considers the service quality and distance cost of the workers. Secondly, the algorithm convergence speed was increased and the global optimization ability was improved by improving the initialization and coding strategy, position movement strategy and neighborhood search strategy of the discrete glowworm swarm optimization algorithm. Finally, the improved algorithm was used to solve the model. Experimental results on the simulated and real datasets show that, compared with other swarm intelligence algorithms, the proposed algorithm can improve the total score of task allocation by 2% to 25% on datasets with different scales. By considering the service quality of workers, the proposed algorithm can effectively improve the efficiency of task allocation and the final total score.

For the parallel machine scheduling problem of minimizing the maximum completion time, an Improved Discrete Artificial Bee Colony algorithm (IDABC) was proposed by considering the processing efficiency of the machine and the delivery time of the product as well as introducing the mathematical model of the problem. Firstly, a uniformly distributed population and a generation strategy of the parameters to be optimized were achieved by adopting the population initialization strategy, resulting in the improvement of the convergence speed of population. Secondly, the mutation operator in the differential evolution algorithm and the idea of simulated annealing algorithm were used to improve the local search strategy for the employed bee and the following bee, and the scout bee was improved by using the high-quality information of the optimal solution, resulting in the increasement of the population diversity and the avoidance of trapping into the local optimum. Finally, the proposed algorithm was applied in the parallel machine scheduling problem to analyze the performance and parameters of the algorithm. The experimental results on 15 examples show that compared with the Hybrid Discrete Artificial Bee Colony algorithm (HDABC), IDABC has the accuracy and stability improved by 4.1% and 26.9% respectively, and has better convergence, which indicates that IDABC can effectively solve the parallel machine scheduling problem in the actual scene.

To meet high quality requirement of virtual resource service in cloud computing applications and solve the problem that cloud computing task scheduling only consider single objective currently, a Discrete Artificial Bee Colony (DABC) algorithm for cloud task scheduling optimization was proposed by considering the users' shortest waiting time, resource load balancing and economic principle. First, the multi-objective mathematical model of cloud task scheduling was established in theory. Second, by combining with preference satisfaction policy, introducing the local search operator and changing the searching way of scout bee, an optimizing strategy based on the Multi-objective DABC (MDABC) algorithm was proposed to solve the problem. Different cloud task scheduling simulation experimental results show that the proposed MDABC algorithm can obtain higher comprehensive satisfaction than the basic DABC algorithm, Genetic Algorithm (GA) and classical greedy algorithm. Thus, the proposed MDABC algorithm can better improve the performance of cloud task scheduling in virtual resource system, and its universality is better.

The basic Ant Colony Optimization (ACO) has slow searching speed at prior period and being easy to fall into local optimum at later period. To overcome these shortcomings, the initial pheromone distribution strategy and local optimization strategy were proposed, and a new pheromone updating rule was put forward to strengthen the effective accumulation of pheromone. The improved ACO was used in QoS-based Web service composition optimization problem, and the feasibility and effectiveness of it was verified on QWS2.0 dataset. The experimental results show that, compared with the basic ACO, the improved ACO which updates the pheromone with the distance of the solution and the ideal solution, and the improved genetic algorithm which introduces individual domination strength into the environment selection, the proposed ACO can find more Pareto solutions, and has stronger optimizing capacity and stable performance.