For the channel access and resource allocation problem in the p-persistent Mobile Ad hoc NETwork （MANET）， an adaptive channel access and resource allocation algorithm with low complexity was proposed. Firstly， considering the characteristics of MANET， the optimization problem was formulated to maximize the channel utility of each node. Secondly， the formulated problem was then transformed into a Markov decision process and the state， action， as well as the reward function were defined. Finally， the network parameters were trained based on policy gradient to optimize the competition probability， priority growth factor， and the number of communication nodes. Simulation experiment results indicate that the proposed algorithm can significantly improve the performance of p-persistent CSMA （Carrier Sense Multiple Access） protocol. Compared with the scheme with fixed competition probability and predefined p-value， the proposed algorithm can improve the channel utility by 45% and 17%， respectively. The proposed algorithm can also achieve higher channel utility compared to the scheme with fixed number of communication nodes when the total number of nodes is less than 35. Most importantly， with the increase of packet arrival rate， the proposed algorithm can fully utilize the channel resource to reduce the idle period of time slot.

In the training process of manipulator path planning algorithm， the training efficiency of manipulator path planning is low due to the huge action space and state space leading to sparse rewards， and it becomes challenging to evaluate the value of both states and actions given the immense number of states and actions. To address the above problems， a robotic manipulator planning algorithm based on SAC （Soft Actor-Critic） reinforcement learning was proposed. The learning efficiency was improved by incorporating the demonstrated path into the reward function so that the manipulator imitated the demonstrated path during reinforcement learning， and the SAC algorithm was used to make the training of the manipulator path planning algorithm faster and more stable. The proposed algorithm and Deep Deterministic Policy Gradient （DDPG） algorithm were used to plan 10 paths respectively， and the average distances between paths planned by the proposed algorithm and the DDPG algorithm and the reference paths were 0.8 cm and 1.9 cm respectively. The experimental results show that the path imitation mechanism can improve the training efficiency， and the proposed algorithm can better explore the environment and make the planned paths more reasonable than DDPG algorithm.

A free search algorithm was proposed for the discrete optimization problem. However，solutions simply got from free search algorithm often have crossover phenomenon. Then, an algorithm free search algorithm combined with cross elimination was put forward, which not only greatly improved the convergence rate of the search process but also enhanced the quality of the results. The experimental results using Traveling Saleman Problem (TSP) standard data show that the performance of the proposed algorithm increases by about 1.6% than that of the genetic algorithm.

A satellite antenna characterized by wide-beam, wide-bandwidth and microwave right-hand-circular polarization was designed. First, the Dynamic Dominant Evolution Algorithm (DDEA) was used to search globally on a parallel computing platform. Then orthogonal design and HFSS software based on finite element method were used to search within local scope evenly and elaborately, so that the antenna gain was further improved. The improved antenna meets the requirements on beam and saves the feeding power of satellite.

This paper proposed an earthquake prediction method based on neural networks and an improved particle swarm optimization algorithm. In this method, a feed forward neural network was applied to predict the level of earthquake, and a modified particle swarm optimization algorithm was applied to optimize the neural network model. In order to get weights of the optimal balance between the global search and local search, a Dynamic Mutational Particle Swarm Optimization (DMPSO) algorithm was designed by using the ideology of dynamic mutation. This algorithm was used to adjust weights of the feed forward neural network. The simulation results of the proposed method were compared with the simulation results of two feed forward networks with different training algorithms. The comparison results show that the prediction model with DMPSO has fastest convergence rate, the smallest prediction error and strongest generalization ability. In conclusion, the model with DMPSO is a good reference to the middle earthquake prediction.