遗传粒子群优化的DV-Hop定位算法

用常规粒子群改进的DV-Hop算法由于粒子易陷入局部最优而导致较大的定位误差,对此,提出了结合遗传粒子群的DV-Hop定位(GAPSO-DV-Hop)算法.首先根据最大理想跳数筛选锚节点,计算加权平均每跳距离,权重采用锚节点之间距离、最小跳数、以及通信半径构成;其次,用遗传机制改进粒子群算法以代替最小二乘法,所作改进包括使用前摄估计缩小粒子搜索范围、根据遗传算法中的交叉策略生成待交叉粒子队列,并在每次迭代后选取最差个体做动态变异.仿真结果表明,在相同环境下,所提GAPSO-DV-Hop算法的定位精度明显高于常规DV-Hop算法以及其他对比算法.

Genetic PSO Improved DV-Hop Localization Algorithm

Larger location errors could occurs in the results given by the distance vector-hop(DV-Hop)algorithm improved with the particle swarm optimization(PSO)because of the possible local optimization of the PSO.A DV-Hop algorithm combined with the genetic PSO(GAPSO-DV-Hop)is proposed for the problem.Firstly,the anchor nodes are selected according to the maximal ideal hops.Then the weighted average hop distance is calculated with the weights constructed from the distance/hops between anchor nodes and communication radius of anchor nodes.Secondly,the genetic-improved PSO is employed to replace the least square method.The improvements include shrinking the hunting area using proactive estimate,producing the particle queue according the crossover strategy,and making the worst individual dynamical mutating after each iteration.The simulation results show that the positioning accuracies given by the proposed GAPSO-DV-Hop algorithm are obviously better than those by the conventional DV-Hop and other referred algorithms.