基于自适应差分优化的改进虚拟力导向粒子群算法研究

Research on improved virtual force-directed particle swarm based on adaptive differential optimization

为提高节点在无线传感器网络中的自部署性能,本文以虚拟力导向粒子群算法为基石,在部署区域内,采用正方形网格划分方式,并引入一种改进的自适应差分进化策略,对原算法进行改进。改进算法引入了移动目的地对移动节点的引力作用,并通过自适应调整,有目的的向扩大网络覆盖率的目标进化,从而最大限度地优化节点的部署速度和网络的覆盖率。通过对该算法的性能进行了仿真与分析,在网络覆盖率、算法收敛速度以及部署时间等方面,相比于经典虚拟力算法及虚拟力导向粒子群算法,该算法具有更佳的部署性能。

In order to ameliorate the performance of WSN nodes＇ optimization deployment, this paper is based on the virtual force directed particle swarm optimization, applied an improved adaptive differential evolution strategy which comes from the deployment area square mesh to improve the original algorithm. The algorithm introduces a mobile destination gravitational pull on the mobile node with a purpose to expand the network coverage objective evolutionary though adaption, then, it optimizes the deployment speed of the nodes and the coverage rate of＇ the network uttermost. This algorithm performs better in coverage, iterations and deployment time when compared with the virtual force algorithm and the virtual force directed particle swarm optimization, through the deployment of the performance of algorithms for simulation and analysis.