基于力导向量子粒子群算法的气体源点定位研究

Study of Plume Source Localization Based on Force-Directed QPSO Algorithm

根据有风时气体浓度衰减模型,采用量子粒子群优化(quantum particle swarm optimization,QPSO)算法实现无线传感网络中的气体源点定位,考虑到传感器节点测量气体浓度时存在门限值的实际情况,引入力导向思想,通过使传感器节点产生虚拟力来影响QPSO算法的位置更新过程,使粒子移动更有目的性,引导粒子进化,加快算法收敛.仿真结果表明:在不同噪声条件下,与QPSO算法相比,力导向QPSO定位算法具有更强的鲁棒性,收敛速度更快,定位精度更高,更能获取问题的最优解.

Based on the attenuation model of a plume source in the wind field, this paper firstly apply a quantum particle swarm optimization（QPSO） to solve the plume source localization problem in wireless sensor networks, according to the fact that the plume source can only be detected when the sensor measured concentration is lager than a threshold, we adopt the force-directed heuristics that those sen- sors exert virtual forces which affect the updated position of each particle in QPSO, make the particles random roaming more purposive, direct the updating of particles for improving the convergence speed. Simulation results show that compared with QPSO in the different noise conditions, the virtual force-directed QPSO is more robust, can more quickly find the optimal solution of problem and the precision is higher.