基于三维自适应果蝇优化算法的智能车间无线传感器网络位置优化

Shaobo Li, Chenglong Zhang, Jinglei Qu
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引用次数: 4

摘要

现代制造业的生产过程是复杂多变的,制造资源具有广泛的动态特征。为了有效地管理和控制制造资源,实现智能车间位置数据的实时采集,提出了一种基于无线传感器网络的制造资源位置感知体系结构。为保证智能车间制造资源位置数据的实时性,设计了一种三维自适应果蝇优化算法来估计位置坐标,该算法引入自适应惯性权重系数,保留了果蝇优化算法局部搜索能力强的优点,提高了全局优化能力。有效地解决了智能车间的三维定位问题。仿真结果表明,本文算法应用于三角测量的定位计算,定位误差较小,运行时间较短,提高了定位数据的准确性,满足了智能车间人员、物料、物流车辆等制造资源的实时定位要求,便于资源感知和调度管理,从而提高了管理水平和产品质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm
The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinates, the new algorithm introduced the adaptive inertial weight coefficient, retained the advantage of strong local search ability of fruit fly optimization algorithm, improved the ability of global optimiza-tion, effectively solved the problem of three-dimensional location in intelligent workshop. The simulation results show that, the algorithm in this paper is applied to the location calculation of triangulation, which has smaller location error and shorter operation time, it improves the accuracy of the location data and meets the real-time location requirements of manufacturing resources such as intelligent workshop staff, materials, logistics vehicles etc. facilitate resource sensing and scheduling management, thereby improving management standards and product quality.
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