The Multidimensional Knapsack Problem (MKP) is a kind of typical multi-constraint combinatorial optimization problems. In order to solve this problem, a Greedy Binary Lion Swarm Optimization (GBLSO) algorithm was proposed. Firstly, with the help of binary code transform formula, the locations of lion individuals were discretized to obtain the binary lion swarm algorithm. Secondly, the inverse moving operator was introduced to update the location of lion king and redefine the locations of the lionesses and lion cubs. Thirdly, the greedy algorithm was fully utilized to make the solution feasible, so as to enhance the local search ability and speed up the convergence. Finally, Simulations on 10 typical MKP examples were carried out to compare GBLSO algorithm with Discrete binary Particle Swarm Optimization (DPSO) algorithm and Binary Bat Algorithm (BBA). The experimental results show that GBLSO algorithm is an effective new method for solving MKP and has good convergence efficiency, high optimization accuracy and good robustness in solving MKP.

Hypernetwork is a kind of networks which is more complex than the ordinary complex network. Hypernetwork can describe complex system existing in the real world more appropriately than complex network since every hyperedge of it can connect any number of nodes. A new method to measure hypernetwork-Hypernetwork Dimension (HD) was proposed aiming to the shortcomings and deficiencies of existing measure method of hypernetwork. Hypernetwork dimension was expressed as twice as much as the ratio of the logarithm of the sum of all nodes' weights and product of corresponding hyperedge's weight in all hyperedges to the logarithm of the product of sum of hyperedges' weights and sum of nodes' weights. The hypernetwork dimension was able to be applied to the weighted hyperworks with many different numerical types of both nodes' weights and hyperedges' weights, such as positive real numbers, negative real numbers, pure imaginary numbers, and even complex numbers. Finally, several important properties of the proposed hypernetwork dimension were discussed.

For a Mobile Crowd Sensing (MCS) network has a large group of participants, and the acquisition and submission of tasks are almost unrestricted, so that data redundancy is high and data quality cannot be guranteed. To solve the problem, a method called Participant Reputation Evaluation Scheme (PRES) was proposed to evaluate the data quality and the reputation of participants. A participant's reputation was evaluated from five aspects:response time, distance, historical reputation, data correlation and quality of submitted data. The five parameters were quantified, and a regression equation was established by using logistic regression model to get the participant reputation after submitting data. The reputation credibility of a participant was in the interval[0.0, 1.0], and concentrated in[0.0,0.2] and[0.8, 1.0], making it easier for the group of mental perception network to choose appropriate participants, and the accuracy of the evaluation results by the crowd sensing showed that PRES was more than 90%.

To solve problems of population diversity and convergence speed when an Ant Colony System (ACS) is used to robot path planning, a dynamic chaos operator was introduced in the ACS. The dynamic chaotic ACS can balance population diversity and convergence speed. The core of dynamic chaotic ACS is that a Logistic chaotic operator was added to the traditional ACS to increase population diversity and improve the quality of the solutions. First, the chaotic operator was added to the pre-iteration to adjust the global pheromone value in the path to increase the population diversity of the algorithm, so as to avoid the algorithm to fall into the local optimal solution. Then, in the later stage, the ACS was used to ensure convergence speed of the dynamic chaotic ACS. The experimental results show that the dynamic chaotic ACS has better population diversity compared with the ACS for the robot path planning problem. The solution quality is higher and the convergence speed is faster. Compared with the Elitist Ant colony System (EAS) and the rank-based Ant System (ASrank), the dynamic chaotic ACS can balance the relationship between the quality of the solutions and the convergence speed. The dynamic chaotic ACS can find better optimal solutions even in the complex obstacle environment. The dynamic chaotic ACS can improve the efficiency of mobile robot path planning.

The traditional sparse coding image classification algorithms extract single type features, ignore the spatial structure information of the images, and can not make full use of the feature topological structure information in feature coding. In order to solve the problems, a image classification algorithm based on multi-scale feature fusion and Hessian Sparse Coding (HSC) was proposed. Firstly, the image was divided into sub-regions with multi-scale spatial pyramid. Secondly, the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) were effectively merged in each subspace layer. Then, in order to make full use of the feature topology information, the second order Hessian energy function was introduced to the traditional sparse coding target function as a regularization term. Finally, Support Vector Machine (SVM) was used to classify the images. The experimental results on dataset Scene15 show that, the accuracy of HSC is 3-5 percentage points higher than that of Locality-constrained Linear Coding (LLC), while it is 1-3 percentage points higher than that of Support Discrimination Dictionary Learning (SDDL) and other comparative methods. Time-consuming experimental results on dataset Caltech101 show that, the time-consuming of HSC is about 40% less than that of the Multiple Kernel Learning Sparse Coding (MKLSC). The proposed HSC can effectively improve the accuracy of image classification, and its efficiency is also better than the contrast algorithms.

The Pointwise Mutual Information (PMI) boundary detection algorithm can achieve the boundary of each image accurately, however the efficiency is restricted by the redundancy and randomness of sampling process. In order to overcome the disadvantage, a new method based on the middle structure information provided by super-pixel segmentation was proposed. Firstly, the image was divided into approximately the same super-pixels in the pre-processing. Secondly, the sampling points were located in adjacent different super-pixels which made the sample selection be more ordered, and the image information could still be extracted effectively and completely even though the total number of sampling points was reduced sharply. The comparison experiment of the proposed algorithm and the original PMI boundary detection algorithm was carried out on the Berkeley Segmentation Data Set (BSDS). The results show that the proposed algorithm achieves 0.7917 AP (Average Precision) under PR (Precision/Recall) curve with 3500 sample points, while the original algorithm needs 6000 pairs. It confirms that the proposed algorithm can guarantee the detection accuracy with reducing sample points, which improves the real-time performance effectively.