An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment
An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment
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DOI：
10.3390/s7030354
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发表时间：
2007-03-01
期刊：
影响因子：
3.9
通讯作者：
Ma, Jun-Jie
中科院分区：
文献类型：
--
作者：
Wang, Xue;Wang, Sheng;Ma, Jun-Jie
无线传感器网络的有效性取决于动态部署所提供的覆盖范围和目标检测概率，而虚拟力算法通常支持动态部署。然而，在VF算法中，固定传感器节点施加的虚拟力会阻碍移动的传感器节点的移动。粒子群优化（PSO）是另一种动态部署算法，但在这种情况下，所需的计算时间是一个大的瓶颈。提出了一种基于虚拟力的协同进化粒子群优化算法（VFCPSO），该算法将协同进化粒子群优化算法（CPSO）与虚拟力算法（VF）相结合，由此，CPSO使用多个群来优化解向量的不同分量，以协同地进行动态部署，并且每个粒子的速度不仅根据历史局部和全局最优解，也是传感器节点的虚拟力。仿真结果表明，VFCPSO算法能够满足无线传感器网络的动态部署要求，在计算时间和有效性方面均优于VF、PSO和VFPSO算法。
The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named "virtual force directed co-evolutionary particle swarm optimization" (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.