With the rapid growth of computing power, the accumulation of training data and the improvement of nonlinear activation function, Convolutional Neural Network (CNN) has a good recognition performance in handwritten Chinese character recognition. To solve the problem of slow speed of CNN for handwritten Chinese character recognition, Two Dimensional Principal Component Analysis (2DPCA) and CNN were combined to identify handwritten Chinese characters. Firstly, 2DPCA was used to extract the projection eigenvectors of handwritten Chinese characters. Secondly, the obtained projection eigenvectors were formed into an eigenmatrix. Thirdly, the formed eigenmatrix was used as the input of CNN. Finally, the softmax function was used for classification. Compared with the model based on AlexNet, the proposed method has the running time reduced by 78%; and compared with the model based on ACNN and DCNN, the proposed method has the running time reduced by 80% and 73%, respectively. Experimental results show that the proposed method can reduce the running time of handwritten Chinese character recognition without reducing the recognition accuracy.

The multi-agent collaborative pursuit problem is a typical problem in the multi-agent coordination and collaboration research. Aiming at the pursuit problem of single escaper with learning ability, a multi-agent collaborative pursuit algorithm based on game theory and Q-learning was proposed. Firstly, a cooperative pursuit team was established and a game model of cooperative pursuit was built. Secondly, through the learning of the escaper’s strategy choices, the trajectory of the escaper’s limited Step-T cumulative reward was established, and the trajectory was adjusted to the pursuer’s strategy set. Finally, the Nash equilibrium solution was obtained by solving the cooperative pursuit game, and the equilibrium strategy was executed by each agent to complete the pursuit task. At the same time, in order to solve the problem that there may be multiple equilibrium solutions, the virtual action behavior selection algorithm was added to select the optimal equilibrium strategy. C# simulation experiments show that, the proposed algorithm can effectively solve the pursuit problem of single escaper with learning ability in the obstacle environment, and the comparative analysis of experimental data shows that the pursuit efficiency of the algorithm under the same conditions is better than that of pure game or pure learning.

For the problems of scope decision-making of maintenance for civil aviation engine module and cost optimization of full-life maintenance, the engine module maintenance level decision-making and cost optimization model based on annealing frog leaping particle swarm optimization algorithm with return time interval as variable was proposed. Firstly, according to the maintenance logic diagram for each module in maintenance instruction manual and the replacement situation of life-limited parts, the engine shop visit cost function was built. Secondly, by using the annealing frog leaping particle swarm optimization algorithm, the shop visit costs of different return times and the maintenance level for each module in full life time were determined. Finally, based on examples, the proposed algorithm was compared with the basic particle swarm optimization algorithm, annealing particle swarm optimization algorithm and shuffled frog leaping optimization algorithm, and the influence of different return times on maintenance cost and reliability was analyzed. Experimental results indicate that, when the engine has five shop visits in its full life time, the average cost obtained using annealing frog leaping particle swarm optimization algorithm was 322.479 1 $/flight hour, which was the optimum value compared with those of the other three optimization algorithms. The proposed algorithm can facilitate the shop visit decision-making of airlines and overhaul companies.

A two-stage path planning algorithm was proposed for multi-Agent path planning. Firstly, an improved ant colony algorithm was used to plan an optimal path for each Agent from the starting point to the target point without colliding with the static obstacles in the environment. The reverse learning method was introduced to an improved ant colony algorithm to initialize the ant positions and increase the global search ability of the algorithm. The adaptive inertia weighted factor in the particle swarm optimization algorithm was used to adjust the pheromone intensity Q value to make it adaptively change to avoid falling into local optimum. The pheromone volatilization factor ρ was adjusted to speed up the iteration of the algorithm. Then, if there were dynamic collisions between multiple Agents, the game theory was used to construct a dynamic obstacle avoidance model between them, and the virtual action method was used to solve the game and select multiple Nash equilibria, making each Agent quickly learn the optimal Nash equilibrium. The simulation results show that the improved ant colony algorithm has a significant improvement in search accuracy and search speed compared with the traditional ant colony algorithm. And compared with Mylvaganam's multi-Agent dynamic obstacle avoidance algorithm, the proposed algorithm reduces the total path length and improves the convergence speed.

Formation obstacle avoidance is one of the key issues in the research of multi-agent formation. Concerning the obstacle avoidance problem of multi-agent formation in dynamic environment, a new formation obstacle avoidance method based on Artificial Potential Field (APF) and Cuckoo Search algorithm (CS) was proposed. Firstly, in the heterogeneous mode of dynamic formation transformation strategy, APF was used to plan obstacle avoidance for each agent in multi-agent formation. Then, in view of the limitations of APF in setting attraction increment coefficient and repulsion increment coefficient, the idea of Lěvy flight mechanism in CS was used to search randomly for the increment coefficients adapted to the environment. The simulation results of Matlab show that, the proposed method can effectively solve the obstacle avoidance problem of multi-agent formation in complex environment. The efficiency function is used to evaluate and analyze the experimental data, which can verify the rationality and effectiveness of the proposed method.

In modeling and simulation of meteor burst communication system, concerning the problem of network delay caused by Hybrid Automatic Repeat Request (HARQ), an estimation model of transmission delay based on HARQ was proposed. Firstly, in consideration of the network structure and channel characters in meteor burst communication, a network delay model was constructed by analyzing the theory of HARQ. Then, based on queuing theories, the improvement mechanism of HARQ was introduced to establish an estimation model of transmission delay of Type-Ⅰ HARQ and one of Type-Ⅱ HARQ. Finally, the simulation was realized to compare and analyze the transmission delay performance of two kinds of HARQ. When packet transmission accuracy or packet transmission time changes independently, the transmission delay of Type-Ⅱ HARQ is less than that of Type-Ⅰ HARQ. The experimential results show that Type-Ⅱ HARQ has advantages of network delay performance in meteor burst communication compared to Type-Ⅰ HARQ.

Aiming at the problems of the path planning algorithm, such as slow convergence and low efficiency, a multi-Agent path planning algorithm based on hierarchical reinforcement learning and artificial potential field was proposed. Firstly, the multi-Agent operating environment was regarded as an artificial potential field, the potential energy of every point, which represented the maximal rewards obtained according to the optimal strategy, was determined by the priori knowledge. Then, the update process of strategy was limited to smaller local space or lower dimension of high-level space to enhance the performance of learning algorithm by using model learning without environment and partial update of hierarchical reinforcement learning. Finally, aiming at the problem of taxi, the simulation experiment of the proposed algorithm was done in grid environment. To close to the real environment and increase the portability of the algorithm, the proposed algorithm was verified in three-dimensional simulation environment. The experimental results show that the convergence speed of the algorithm is fast, and the convergence procedure is stable.

Traditional hypernetwork model is biased towards the majority class, which leads to much higher accuracy on majority class than the minority when being tackled on imbalanced data classification problem. In this paper, a Boosting ensemble of cost-sensitive hypernetworks was proposed. Firstly, the cost-sensitive learning was introduced to hypernetwork model, to propose cost-sensitive hyperenetwork model. Meanwhile, to make the algorithm adapt to the cost of misclassification on positive class, cost-sensitive hypernetworks were integrated by Boosting. The proposed model revised the bias towards the majority class when traditional hypernetwork model was tackled on imbalanced data classification, and improved the classification accuracy on minority class. The experimental results show that the proposed scheme has advantages in imbalanced data classification.

The coordination problem between Agents in traffic intersections is a gambling problem. On the basis of bounded rationality, this paper tentatively made use of game learning thought to build the multi-Agent coordinate game learning algorithm. This learning coordination algorithm analyzed travelers' unreasonable behavior and corrected it to realize the urban traffic intersections unimpeded, so as to achieve regional and global transportation optimization. At last, its feasibility is verified by means of an example and simulation.

The Artificial Fish Swarm Algorithm (AFSA) has low search speed and it is difficult to obtain accurate value. To solve the problems, an improved algorithm based on social learning mechanism was proposed. In the latter optimization period, the authors used convergence and divergence behaviors to improve the algorithm. The two acts had fast search speed and high optimization accuracy, meanwhile, the divergence behavior enhanced the population diversity and the ability of skipping over the local extremum. To a certain extent, the improved algorithm enhanced the search performance. The experimental results show that the proposed algorithm is feasible and efficacious.

For the complex tasks with time constraints, which can dynamically be added to environment, a task allocation model based on game theory was established, and a task allocation method was proposed, which made Computer Generated Actor (CGA) be able to choose its actions according to the local information owned by itself, and ensured that CGA learned a strict pure strategy Nash equlilibrium quickly by using fictitious play method on behavior coordination. The simulation results show that this method is reasonable, and it can effectively solve the dynamic task allocation problem.

Communication is the most effective and direct method of coordinating and cooperating among multi-Agents, but the cost of communication restricts the use of this method. In order to reduce traffic subject in the coordination of Multi-Agent System (MAS), this paper put forward a heuristic algorithm, which would make Agents choose the observation that is beneficial to team performance to communicate. The experimental results show that choosing beneficial observation to communicate could ensure the efficiency of limited communication bandwidth and improve system performance.

In topic tracking, the initial topic related stories are few and topic evolves dynamically, which leads to the topic model could not express topic accurately. Concerning this problem, it was proposed to build amended topic model by feedback stories collected by dynamic threshold, to amend topic model dynamically. And in combination of the feature that the named entity could differentiate different topics more effectively, it was suggested to increase the weight of named entity when amending topic model, to express a topic better. The experimental results indicate that, this method can solve the topic shifting problem effectively, and the miss tracking rate and fault tracking rate decrease a lot in topic tracking.

Group key agreement is one of the important stages to carry out secure multicast communication. A group controller node switch method was given pointing to the problem of satellite network topology changed dynamically. It could adjust controlling nodes in a dynamic way. Then, both authentication and integrality mechanism were used to attest agreement messages and group keys, a group key generation and renewing method was proposed, which could improve security of agreement messages. The results of simulation and analysis show that this group key agreement protocol leads to high efficiency and security.

Aggregate nearest neighbor query involves many query points, so it is more complicated than traditional nearest neighbor query, and the distribution characteristic of query set implies the region where its aggregate nearest neighbor exists. Taking full account of the distribution characteristic of query set, a method by utilizing distribution characteristic to direct the way of aggregate nearest neighbor searching was given. Based on the method, a new algorithm named AM was presented for aggregate nearest neighbor query. AM algorithm can dynamically capture and use the distribution characteristic of query set, which enables it to search data points in a right order, and avoid unnecessary searching to data points. The experimental results show the efficiency of the algorithm.