寻优能力增强型越界免疫粒子群算法

Optimized Ability Enhancement and out of Bounds Immune Particle Swarm Optimization

PSO算法是提高WSN覆盖的一种全局优化算法。针对布尔感知模型与实际情况有所差别,且存在粒子搜索速度变慢的问题。提出了一种寻优能力增强型越界免疫粒子群算法(optimized ability enhancement and out of bounds immune PSO,OAEBI-PSO),采用概率感知模型,在粒子越界和粒子更新两方面做出了改进,得到了更高的覆盖率,并且避免陷入局部最优。仿真表明,该算法能够平均提高11%的覆盖率,并且通过50次的蒙特卡罗实验,表明该算法具有较强的稳定性。

PSO algorithm is a global optimization algorithm for improving WSN coverage. In view of the problem that the Boolean perception model is different from the actual situation and the particle search speed would be slow. An optimized ability enhancement and out of bounds immune PSO(OAEBI-PSO) are proposed, the probabilistic perception model is adopted and the algorithm in the particle bounds and the particle update are improved, as a consequence, the higher coverage ratio is obtained and local optima is avoided. Simulation results show that the algorithm can improve the average coverage by 11%, and the algorithm has a strong stability through the 50 times Monte-Carlo experiment.