基于多目标粒子群优化算法的传感器定位方法研究  

Research on Sensor Location Method Based on Multi-objective Particle Swarm Optimization

作　　者：吕达[1] Lv Da(Electric Engineering Department,Baotou Vocational&Technical College,Baotou,Inner Mongolia 014030)

机构地区：[1]包头职业技术学院电气工程系,内蒙古包头014030

出　　处：《包头职业技术学院学报》2019年第1期21-24,共4页Journal of Baotou Vocational & Technical College

基　　金：内蒙古自治区高等学校科学研究项目(NJZY16463)成果

摘　　要：为了解决粒子群算法的传感器定位方法,提出基于多目标粒子群的位置传感器的定位覆盖优化方法。分析基本粒子群算法进行传感器位置优化的过程,找出其存在的局部收敛问题,通过采用位置更新和惯性权重两种拟物方案,在粒子速度进化过程中对多目标粒子群算法的速度修正过程实施优化,降低重复覆盖率,完成传感器定位的优化。实验结果表明,改进基于多目标粒子群算法具有更快的收敛效率,对传感器定位的优化效果更好。In order to solve the problem of local convergence of particle swarm optimization(PSO)for sensor location,a location coverage optimization method based on multi-objective particle swarm optimization(MPSO)is proposed.The process of sensor location optimization based on basic particle swarm optimization(PSO)is analyzed,and the local convergence problem is found.By using two quasi-physical schemes of position updating and inertia weight,the speed correction process of multi-objective particle swarm optimization(MPSO)is optimized in the process of particle velocity evolution,which reduces the repetitive coverage and completes the optimization of sensor location.The experimental results show that the improved multi-objective particle swarm optimization algorithm has faster convergence efficiency and better optimization effect for sensor location.

关 键 词：多目标粒子群 传感器 定位优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP212[自动化与计算机技术—控制科学与工程]