基于改进平衡优化算法的多无人机网络覆盖技术

针对通信受限区域网络覆盖优化问题，提出一种改进平衡优化算法，提高多无人机编队飞行网络覆盖率。分析无人机网络覆盖特性，对多无人机进行网络覆盖面积建模，完成无人机数量预估；采用随机反向学习机制生成平均初始种群个体，提高了平衡池候选解的样本分布；对候选解采用莱维飞行方法进行优化迭代，提高了算法的收敛速度；并引入非线性递减的变种群数量策略，动态调整平衡算法的粒子数量，有效地提升了算法的计算效率。设计算法仿真样例，仿真结果表明，所设计的改进型算法比传统的平衡优化算法迭代次数减小近50%,同时目标区域的网络覆盖率得到显著提升。

An Improved Equilibrium Optimization Algorithm for Multi-UAV Network Coverage Technology

Aiming at the problem of network coverage optimization in communication-constrained areas , an improved equilibrium optimizer algorithm is proposed to increase the network coverage of multi UAV formation flight.According to the characteristics of UAV network coverage , the model of multi UAV network coverage is developed and the number of UAVs is estimated.The random reverse learning mechanism is used to generate the average initial population individuals , which improves the sample distribution of the candidate solutions of the balance pool.The Levy flight method is used to optimize the candidate solution , which improves the convergence speed of the algorithm.In addition , a non-linear decreasing variable population number strategy is introduced to dynamically adjust the number of particles in the equilibrium optimizer-algorithm , which effectively raises the computational efficiency of the algorithm.A simulation example of the algorithm is designed.The simulation results show that the improved algorithm designed in this paper reduces the number of iterations nearly 50% as compared to the traditional balanced optimization algorithm , at the same time , the network coverage of the target area is significantly increased.