混合粒子群和差分进化的定位算法

针对由测量误差造成的无线传感器网络定位精度不高的问题，提出一种混合粒子群和差分进化的节点定位算法（HPSO-DE）。首先，对粒子群算法的惯性权重进行自适应更新，使得每个个体随着迭代次数的增加而增大，进而提高其全局探索能力，然后改进差分进化算法的变异策略，从而提高该算法的局部寻优能力，之后将个体先经过改进的粒子群算法优化，低于平均适应度值的个体继续通过改进的差分进化算法优化，从而得到HPSO-DE算法。HPSO-DE算法继承了二者的优点，提高了该算法的最优解精度和收敛速度。最后在无线传感器网络节点定位模型中应用HPSO-DE算法，仿真结果表明，所提HPSO-DE算法在测距误差为30%时，定位误差比PSO和DFOA分别少2.1m和1.1m，具有更高的定位精度和更强的抗误差性能。

Hybrid particle swarm optimization and differential evolution localization algorithm

Aiming at the problem of low positioning accuracy caused by ranging error in Wireless Sensor Networks, a hybrid particle swarm optimization and differential evolution algorithm (HPSO-DE) for node localization is proposed. Firstly, the inertia weight of PSO is updated adaptively to improve its global exploring ability, so that each individual increases with the iterations, thereby improving its global exploration ability, and then improving the mutation strategy of the differential evolution algorithm to improve the locality of the algorithm. After the individual is optimized by the improved particle swarm optimization algorithm, and the individuals below the average fitness value continue to be optimized by the improved differential evolution algorithm to obtain the HPSO-DE algorithm. The HPSO-DE algorithm inherits the advantages of both, and improves the optimal solution precision and convergence speed of the algorithm. Finally, the HPSO-DE algorithm is applied to the wireless sensor network node location model. The simulation results show that the proposed HPSO-DE algorithm has a positioning error of 2.1m and 1.1m less than PSO and DFOA, respectively, when the ranging error is 30%, and has a high positioning accuracy and greater resistance to errors.