To mine the process model including multi-concurrent 2-loops of triangles in incomplete logs, an AlphaMatch algorithm based on extended Alpha algorithm was proposed. Two activities in triangle structure could be correctly matched in 2-loops of triangles by AlphaMatch in the log without repeated activity sequence, thus the process model with multi-concurrent 2-loops of triangles could be mined. Firstly, the activities in 2-loops of triangles were divided into two categories according to the number of activities. Then, a matrix of head and tail position of the activities was constructed to match the two categories and a footprint matrix was constructed to show the relationship between activities. Finally, a large number of experiments were carried out on ProM platform from model correctness, mining efficiency, fitness and precison. Experimental results show that the Petri net model including multi-concurrent 2-loops of triangles can be mined efficiently by the proposed algorithm.

In order to improve the quality and efficiency of railway crew rostering plan arrangement, the problem of crew rostering plan arrangement was abstracted as a Multi-Traveling Salesman Problem (MTSP) with single base and considering mid-way rest, a single-circulation crew rostering plan mathematical model aiming at the smallest rostering period and the most balanced distributed redundant connection time between crew routings was established, and a new amended heuristic ant colony optimization algorithm was proposed aiming at the model. Firstly, a solution space satisfying the spatial-temporal constraints was constructed and the pheromone concentration was set for the crew routing nodes and the continued paths respectively. Then, the amended heuristic information was adopted to make the ants start at the crew routing order and go through all the crew routings. Finally, the optimal crew rostering plan was selected from the different crew rostering schemes. The proposed model and algorithm were tested on the data of the intercity railway from Guangzhou to Shenzhen. The comparison results with the plan arranged by particle swarm optimization show that under the same model conditions, the crew rostering plan arranged by amended heuristic ant colony optimization algorithm has the average monthly man-hour reduced by 8.5%, the rostering period decreased by 9.4%, and the crew overwork rate of 0. The designed model and algorithm can compress the crew rostering cycle, reduce the crew cost, balance the workload, and avoid the overwork of crew.

To solve the time-consuming problem of group-by aggregation operation in case of data-intense computation, a cache-friendly group-by aggregation method was proposed. In this paper, the group-by aggregation operation was optimized in two aspects. Firstly, designing cache-friendly group-by aggregation algorithm on Supersonic, an open-source and column-oriented query execution engine, to take the full advantage of column-storage on in-memory computation. Secondly, rewriting the algorithm with multi-threads to speed up the query. In this paper, four different parallel aggregation algorithms were put forward, respectively named Shared-Nothing Parallel Group-by Aggregation (NSHPGA) algorithm, Table-Lock Shared-Hash Parallel Group-by Aggregation (TLSHPGA) algorithm, Bucket-Lock Shared-Hash Parallel Group-by Aggregation (BLSHPGA) algorithm and Node-Lock Shared-Hash Parallel Group-by Aggregation (NLSHPGA) algorithm. Through a series of comparison experiment on different group power set and different number of worker threads, NLSHPGA algorithm was proved to have the best performance both on speed-up ratio and concurrency, which achieved 10x speedups on part of queries. Besides, considering Cache miss and memory utilization, the results shows that NSHPGA algorithm is suitable for smaller group power set, which was 8 in the experiment, and when getting larger, NLSHPGA algorithm performs better than NSHPGA algorithm.

When drift-scan CCD shooting the sky where bright stars are in the filed of view, because of the frame transfer feature, the trailing smear will appear throughout the star image. A fast smear trailing elimination algorithm was proposed by analyzing the imaging mechanism. The method firstly decreased the background non-uniformity by fitting the background, then located smear trailing by calculating the mean gray value of every column in star image and comparing the mean gray values before and after fitting, finally eliminated smear trailing by setting the trailing pixel with the mean gray value after fitting. The experimental results show that the smear trailing is removed completely and the mean deviation of background is apparently reduced, moreover the consuming time of this method is only 20% of that of traditional smear elimination method, which proves the validity of the method.

Multiple Kernel Learning and Support Vector Machine (MKL-SVM) was applied to automatic music genre classification to choose the optimal kernel functions for different features, a method of conducting the optimal kernel function combination into the synthetic kernel function by weighting for music genre classification was proposed. Different optimal kernel functions were chosen for different acoustic features by multiple kernel classification learning, the weight of each kernel function in classification was obtained, and the weight of each acoustic feature in the classification of the genre was clarified, which provided a clear and definite result for the analysis and selection of the feature vector in the classification of music genre. The experiments on the dataset of ISMIR 2011 show that, compared with the traditional single kernel support vector machine classification, the accuracy of the proposed music genre automatic classification method based on MKL-SVM is greatly improved by 6.58%. And the proposed method can more clearly reveal the the different features' impacts on music genre classification results, the classification results has also been significantly improved by selecting features with larger effects on classification.

To overcome slow convergence velocity of Particle Swarm Optimization (PSO) which falls into local optimum easily, the paper proposed a new approach, a PSO algorithm using opposition-based learning and adaptive escape. The proposed algorithm divided states of population evolution into normal state and premature state by setting threshold. If popolation is in normal state, standard PSO algorithm was adopted to evolve; otherwise, it falls into "premature", the algorithm with opposition-based learning strategy and adaptive escape was adopted, the individual optimal location generates the opposite solution by opposition-based learning, increases the learning ability of particle, enhances the ability to escape from local optimum, and raises the optimizing rate. Experiments were conducted on 8 classical benchmark functions, the experimental results show that the proposed algorithm has better convergence velocity and precision than classical PSO algorithm, such as Fully Imformed Particle Swarm optimization (FIPS), self-organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients (HPSO-TVAC), Comprehensive Learning Particle Swarm Optimizer (CLPSO), Adaptive Particle Swarm Optimization (APSO), Double Center Particle Swarm Optimization (DCPSO) and Particle Swarm Optimization algorithm with Fast convergence and Adaptive escape (FAPSO).

In order to solve the problem of exponential increase of label space, an overlapping community discovery algorithm based on latent feature was proposed. Firstly, a generative model for network including overlapping communities was proposed. And based on the proposed generative model, an optimal object function was presented by maximizing the generative probability of the whole network, which was used to infer the latent features for each node in the network. Next, the network was induced into a bipartite graph, and the lower bound of feature number was analyzed, which was used to optimize the object function. The experiments show that, the proposed overlapping community discovering algorithm can improve the recall greatly while keeping the precision and execution efficiency unchanged, which indicates that the proposed algorithm is effective with the exponential increase of label space.

The standard Particle Swarm Optimization (PSO) suffers from the premature convergence problem and the slow convergence speed problem when solving complex optimal problems, so a Heterogenous PSO with Multi-strategy parallel learning (MHPSO) was presented. Firstly two new learning strategies, named local disturbance learning strategy and Gaussian subspace learning strategy respectively, were proposed to maintain the population's diversity and jump out from the local optima. And an efficient and stable strategy pool was constructed by combing the above two strategies with the existed one (MBB-PSO); Secondly, a simpler and more effective strategy change mechanism was proposed, which could guide particles when to change the learning strategy. The experimental study on a set of classical test functions show that the proposed approach improves the solution accuracy and convergence speed greatly, and has a superior performance in comparison with several other improved PSO algorithms, such as APSO (Adaptive Particle Swarm Optimization).

As standard Particle Swarm Optimization (PSO) algorithm has some shortcomings, such as getting trapped in the local minima, converging slowly and low precision in the late of evolution, a new improved PSO algorithm based on Gaussian disturbance (GDPSO) was proposed. Gaussian disturbance was put into in the personal best positions, which could prevent falling into local minima and improve the convergence speed and accuracy. While keeping the same number of function evaluations, the experiments were conducted on eight well-known benchmark functions with dimension of 30. The experimental results show that the GDPSO algorithm outperforms some recently proposed PSO algorithms in terms of convergence speed and solution accuracy.

In order to overcome the drawbacks of Particle Swarm Optimization (PSO) that converges slowly at the last stage and easily falls into local minima, this paper proposed a new PSO algorithm with convergence acceleration and adaptive escape (FAPSO) inspired by the Artificial Bee Colony (ABC) algorithm. For each particle, FAPSO conducted two search operations. One was global search and the other was local search. When a particle got stuck, the adaptive escape operator was used to search the particle again. Experiments were conducted on eight classical benchmark functions. The simulation results demonstrate that the proposed approach improves the convergence rate and solution accuracy, when compared with some recently proposed PSO versions, such as CLPSO. Besides, the results of t-test show clear superiority.

Concerning the premature convergence of Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping Algorithm (SFLA), this paper proposed a swarm intelligence optimization algorithm based on the combination of SFLA and PSO. In this algorithm, the whole particle was divided into two equal groups: SFLA and PSO. An information replacement strategy was designed in the process of their iteration: comparing the fitness of PSO with that of SFLA, the worst individual in each subgroup of SFLA would replace some better individuals in PSO when SFLA is better; otherwise, some better individuals in PSO would replace the best individual in each subgroup of SFLA. Meanwhile, a collaborative approach between the two groups was also designed. Since the information replacement strategy could be influenced by the premature convergence problem in PSO, a random disturbance would be given on each particle's best position. The simulation results show that the proposed algorithm can improve the global search ability and convergence speed efficiently. For the complex functions with high-dimension, the algorithm has very good stability.