基于改进人工萤火虫算法的无线传感网络覆盖优化

Optimization Strategy of Wireless Sensor Networks Coverage Based on Improved Artificial Glowworm Swarm Optimization Algorithm[HS)]

摘要:

为了提高网络资源利用率延长网络生存时间,提出一种基于共轭梯度法改进人工萤火虫算法(CAGSO)的WSN覆盖优化方案；共扼梯度法是利用目标函数的梯度逐步产生共轭方向并将其作为搜索方向的方法,即利用已知点处的梯度构造一组共扼方向并沿这组共扼方向进行搜索,这种方法经有限次迭代必达极小点；首先建立以覆盖率、节点利用率和能量均匀为准则的覆盖优化数学模型,然后采用改进的CAGSO算法求解该模型,从而得出最优覆盖方案；仿真分析说明,相比基本人工萤火虫算法,改进的CAGSO算法优化的网络覆盖率可以达到94.11％,有效实现WSN覆盖优化。

Abstract:

In order to improve the utilization of network resources and prolong the network lifetime, a wireless sensor network coverage optimization Strategy is proposed based on artificial glowworm swarm optimization algorithm(CAGSO). Conjugate gradient method is the use of the gradient of the objective function to construct a set of conjugate direction, and then search along the conjugate direction , this method can get minimum through finite iterations. Firstly, a model of coverage optimization in WSNs is built up by taking network coverage rate, node utilization and node uniformity as the criterion, and then the CAGSO is used to solve the model,and finally got the coverage optimal Strategy.The simulation results show that:compared to the basic AGSO, the CAGSO optimize the network coverage to 94.11%, it can effectively provide the optimal solution of network coverage.