基于反向学习的群居蜘蛛优化WSN节点定位算法

针对启发优化算法在WSN节点定位问题中定位精度不高和收敛速度较慢的缺陷,提出基于反向学习的群居蜘蛛优化WSN节点定位算法.为减少前期随机搜索,所提出算法首先通过Bounding-box方法得到未知节点可能存在的区域,在该区域初始化启发个体,并将加权中心反向学习策略与群居蜘蛛群优化算法相结合,求解未知节点估计位置,提高算法全局搜索能力.仿真结果表明,相比于传统算法,所提出算法收敛速度更快,节点定位精度更高.

WSN node localization based on social spider optimization and opposition based learning

To improve the weakness of low localization accuracy and slow rate of convergence in the heuristic-based node localization algorithm, WSN node localization based on social spider optimization and opposition based learning is proposed. For reducing random search in the previous stage, a strategy of Bounding-box is firstly performed by determining a square area where the heuristic individual will be initialized. Then weighted center opposition based learning is introduced to original social spider optimization to find the best-estimated location of nodes and improve the global searching ability. Simulation results show that compared with the traditional algorithm, the proposed algorithm has faster convergence speed and higher node localization accuracy.