基于改进鸽群优化和马尔可夫链的多无人机协同搜索方法

针对多无人机在协同搜索过程中存在重复搜索、目标静止、搜索效率低的问题，提出基于改进鸽群优化和马尔可夫链的多无人机协同搜索方法.首先，建立类似传感器探测范围的蜂窝状环境模型，降低对搜索区域的重复搜索；其次，建立满足高斯分布的马尔可夫链动态目标运动模型；然后，将柯西扰动引入基本鸽群优化算法的地图和指南针算子，高斯扰动引入地标算子，同时利用模拟退火机制保留次优个体，进而有效缓减基本鸽群优化算法易陷入局部最优的问题.最后，通过仿真实验将本文算法与其他群体智能算法进行比较，结果表明新型算法的合理性和有效性. 

Cooperative search for multi-UAVs via an improved pigeon-inspired optimization and Markov chain approach

Compared with manned aircraft, unmanned aerial vehicles (UAVs) are affordable and convenient for high-risk missions. Therefore, UAVs are increasingly playing an important role in military and civilian fields. Today, UAVs have been exploited to perform special missions carrying some important equipment. However, influenced by the constraints of single UAV's performance and load, it has become a research hotspot that multi-UAVs perform search cooperatively. The process is to minimize the uncertainty of the unknown area and to find the target as much as possible. In terms of cooperation among UAVs, the more effective method based on search map is used. And search process optimization on the basis of distributed model predictive control (DMPC) or traditional swarm intelligence algorithms are adopted, but these methods have some limitations. Due to the behavior of swarm intelligent individual have the characteristics of the decentralization, distribution, and overall self-organization, which match with the requirements of the localization, distribution and robustness of the UAV cooperate search. Nevertheless, the traditional swarm intelligence algorithms have low search efficiency and are easy to fall into local optimum. To solve the problem of repeated search, static targets and low efficiency in cooperative search for multi-UAVs, a method based on improved pigeon-inspired optimization (PIO) and Markov chain was proposed. Firstly, a honeycomb environmental model similar to the sensor detect region was established to reduce repeated search for the area. Secondly, Markov chain with the Gaussian distribution was used to represent dynamic movement of targets. Thirdly, Cauchy mutation and Gaussian mutation were introduced into the map and compass operator and the landmark operator of PIO, respectively. Meanwhile, simulated annealing (SA) mechanism was exploited to reserve the worse individual, which effectively reduced the problem that PIO was easy to fall into local optimum. Finally, the algorithm was compared with other swarm intelligence algorithms through simulation experiments. The results show that the new method is effective and available.