带混沌映射的WSN蝴蝶优化定位算法

为提高无线传感器网络定位的成功率和精度,提出一种带混沌映射的无线传感器网络蝴蝶优化定位算法(CMBOA)。该策略分为测距、计算和优化3个阶段。基于RSSI模型测量目标节点与信标节点之间的信号强度;采用三点估计坐标模型初步计算目标节点的位置坐标集,基于混沌函数生成解集,并作为蝴蝶优化算法的初始解;采用改进的蝴蝶优化算法对上述位置坐标集进行迭代优化。仿真结果表明,该算法比已有的蝴蝶优化算法以及粒子群算法具有更高的成功率和准确性。

Butterfly optimization localization algorithm with chaotic map in wireless sensor networks

To improve the success rate and accuracy of localization in wireless sensor networks, a butterfly optimization localization algorithm with chaotic map for wireless sensor networks (CMBOA) was proposed. The strategy was divided into three stages including ranging, computing and optimization. The signal intensity of the target node and the beacon node was measured based on the RSSI model, and the location coordinates of the target nodes were calculated using the geometric method, and the solution set was generated based on the chaos function as the initial solution of the butterfly optimization algorithm. The butterfly optimization algorithm was used to optimize the location of the target nodes. The simulation results show that the proposed algorithm has higher success rate and accuracy than the algorithms based on traditional BOA and PSO.