| 噪声环境下RSSI定位问题及其求解的果蝇优化算法 |
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| 引用本文： | 郝 娟,张著洪,凃 歆.噪声环境下RSSI定位问题及其求解的果蝇优化算法[J].计算机工程与应用,2018,54(7):121-126. |
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| 作者姓名： | 郝 娟 张著洪 凃 歆 |
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| 作者单位： | 贵州大学 大数据与信息工程学院，贵阳 550025 |
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| 摘 要： | 针对未知节点的定位过度依赖于接收信号强度指示（Received Signal Strength Indicator，RSSI）物理测量的精度问题，将传统RSSI定位模型转化为非约束期望值规划模型，进而设计随机环境下的新型果蝇优化算法寻找未知节点的位置。该算法利用弧形分组将果蝇群均衡划分为子群，对果蝇个体实施混合变异，加速寻优进程，提高收敛速度和寻优精度。比较性的数值实验显示，该算法的收敛速度快，对未知节点的定位精度高，其应用于RSSI定位问题是可行的。
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| 关 键 词： | 接收信号强度指示 期望值规划 果蝇优化 弧形分组 混合变异 |
RSSI localization in noisy environment and its fruit fly optimization algorithm |
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| HAO Juan,ZHANG Zhuhong,TU Xin.RSSI localization in noisy environment and its fruit fly optimization algorithm[J].Computer Engineering and Applications,2018,54(7):121-126. |
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| Authors: | HAO Juan ZHANG Zhuhong TU Xin |
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| Affiliation: | College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China |
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| Abstract: | This paper firstly transforms the conventional RSSI positioning model into a non-constrained expected value programming model. Secondly, one such model is solved by developing a novel fruit fly optimization algorithm in stochastic environments in order to seek the location of the unknown node. In this algorithm, the current fruit fly population is divided into sub-populations by arc grouping, and subsequently a hybrid mutation strategy is implemented to find the optimal solution. Comparative numerical experiments have validated that the algorithm, with high-efficient convergence and high positioning accuracy, is feasible in solving engineering RSSI positioning. |
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| Keywords: | Received Signal Strength Indicator（RSSI） expected value programming fruit fly optimization arc grouping hybrid mutation |
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