| 引用本文: | 黄有方,吴华锋,肖英杰,李军军.不确定环境下基于鲁棒粒子群优化的物流射频识别网络优化[J].控制理论与应用,2014,31(3):319~326.[点击复制] |
| HUANG You-fang,WU Hua-feng,XIAO Ying-jie,LI Jun-jun.Logistics RFID Network Optimization Based on Robust PSO under Uncertain Conditions[J].Control Theory and Technology,2014,31(3):319~326.[点击复制] |
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| 不确定环境下基于鲁棒粒子群优化的物流射频识别网络优化 |
| Logistics RFID Network Optimization Based on Robust PSO under Uncertain Conditions |
| 摘要点击 2047 全文点击 1471 投稿时间：2013-05-22 修订日期：2013-10-14 |
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| DOI编号 10.7641/CTA.2014.30515 |
| 2014,31(3):319-326 |
| 中文关键词 射频识别 网络优化 不确定 鲁棒 粒子群优化 |
| 英文关键词 radio frequency identification (RFID) network optimization uncertain robust particle swarm optimization (PSO) |
| 基金项目 国家自然科学基金资助项目(51279099); 上海市科学技术委员会基金资助项目(12ZR1412500); 上海市教委科研创新基金重点资助项 目(13ZZ124); 上海市教育委员会和上海市教育发展基金会“曙光计划”基金资助项目(12SG40); 交通运输部应用基础研究资助项目 (2013329810300). |
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| 中文摘要 |
| 针对电子标签位置不确定的物流射频识别(radio frequency identification, RFID)网络优化问题, 综合考虑覆盖
率、负载平衡程度、成本, 建立了鲁棒优化模型. 为求解负载平衡程度, 采用基于Korobov点阵的蒙特卡洛方法. 为减少
计算量, 提高算法寻优能力, 提出一种基于不对称时变S–形(Sigmoid)函数的鲁棒粒子群算法(PSO). 样本规模仅取部分
较小整数、部分较大整数. 仅在算法迭代后期, 样本规模期望值大, 保证算法开发精度; 在较多迭代次数中, 样本规模期
望值小, 加快算法探索速度.仿真实验表明, 该方法具有较佳的搜索性能. |
| 英文摘要 |
| To deal with the logistics radio-frequency-identification (RFID) network optimization problem when the
position of the electronic tag is uncertain, we build a robust optimization model in which the coverage rate, the load balance
and the cost is considered. The Monte Carlo method based on Korobov Lattice is applied to calculate the load balance.
A sort of robust particle swarm optimization (PSO) algorithm based on asymmetrical time-varying sigmoid function is
put forward to reduce the computation complexity and enhance the searching ability. Only some small integers and large
integers are employed for the sample size. In the anaphase of the algorithm, the expected value of sample size is large,
thus the exploitation precision is ensured. In most other iterations, the expected value of sample size is small, thus the
exploration speed is accelerated. Simulation results show that this method possesses better searching ability. |
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