基于多目标粒子群优化算法的传感器定位方法研究
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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.
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.
引文
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[2]柯晶,钱积新.应用粒子群优化的非线性系统辨识[J].电路与系统学报,2013,8(4):12-15.
[3]万伟民,刘继忠.基于粒子群算法的机械零件多目标优化设计研究[J].机械设计与制造,2010(5).
[4]张利彪,周春光.基于粒子群算法求解多目标优化问题[J].计算机研究与发展,2014,41(007):1286-1291.
[5]李炳宇,萧蕴诗,汪镭.PSO算法在工程优化问题中的应用[J].计算机工程与应用,2004,40(018):74-76.
[6]姜长元,赵曙光,沈士根.惯性权重正弦调整的粒子群算法[J].计算机工程与应用,2012,48(8):40-42.
[7]王静.多目标粒子群优化算法的研究及应用[D].大庆:东北石油大学,2011.
[8]赵森.基于精英集选择与扩展策略的多目标智能算法研究[D].广州:华南理工大学,2013.
[9]Zitzler E.,Thiele L.Multi-objective evolutionary algorithms:A comparative case study and the strength pareto approach[J].IEEE transactions on Evolutionary Computation,1999,3(4):257-271.