改进算术优化算法的无线传感器网络覆盖

Improved arithmetic optimization algorithm for wireless sensor network coverage

针对算术优化算法在无线传感器网络部署中易陷入局部最优、搜索能力弱和收敛精度不足等问题,提出一种改进的算术优化算法(improved arithmetic optimization algorithm,IAOA)来解决上述问题.首先,融合随机反向学习策略增加种群的多样性,其次引入正弦控制因子改进数学函数加速器,使探索与开发之间更平衡,增强了算法的寻优能力.仿真实验结果表明,改进后的算法具有更快的收敛速度和更高的收敛精度,有效提高了无线传感器网络的覆盖率.

Aiming at the problems that arithmetic optimization algorithm is easy to fall into local optimization, weak search ability and insufficient convergence accuracy in the deployment of wireless sensor networks, improved arithmetic optimization algorithm(IAOA) is proposed to solve the above problems. Firstly, the random reverse learning strategy is integrated to increase the diversity of the population. Secondly, the sinusoidal control factor is introduced to improve the mathematical function accelerator, which makes the exploration and development more balanced, and enhances the optimization ability of the algorithm Simulation results show that the improved algorithm has faster convergence speed and higher convergence accuracy, and effectively improves the coverage of wireless sensor networks.