改进差分进化算法下的无线传感器网络覆盖优化

Coverage Optimization of Wireless Sensor Networks Based on Improved Differential Evolution Algorithm

为了提升无线传感器网络节点的有效覆盖率,设计了一种改进差分进化算法下的网络节点部署优化策略.使用节点的有效覆盖率作为优化因子构造目标函数优化模型,在优化阶段,采用混沌反向学习初始化策略,设置精英群体引导变异向量、参数自适应等机制改进差分进化算法以加快收敛速度、提升节点的网络覆盖率.将该算法在6个基准测试函数上与其他文献的3种算法进行不同维度的实验对比,并应用到无线传感器网络节点的覆盖优化中,仿真结果表明,改进后的差分进化算法能有效加快收敛速度,提高计算精度,提升无线传感器网络的覆盖率.

In order to improve the effective coverage of wireless sensor nodes,a node deployment strategy based onimproved differential evolution algorithm is proposed.The objective function optimization model is constructed by using the effective coverage rate of sensor nodes,in the optimization phase,chaotic opposition-based learning strategy is used to generate initial population,the mechanism of elite group guided mutation vector and parameter adaptive adjustment are set to improve the differential evolution algorithm to speed up convergence.The algorithm is compared with other algorithms in different dimensions on six benchmark functions.and it is applied to coverage optimization of wireless sensor networks.The simulation results show that the improved differential evolution algorithm can effectively accelerate the convergence speed,improve the calculation accuracy and enhance the coverage of wireless sensor networks.