基于改进粒子群算法的行李条码阅读器布局优化

doi:10.11772/j.issn.1001-9081.2016.01.0128

计算机应用 ›› 2016, Vol. 36 ›› Issue (1): 128-132.DOI: 10.11772/j.issn.1001-9081.2016.01.0128

基于改进粒子群算法的行李条码阅读器布局优化

高庆吉, 李永胜, 罗其俊

中国民航大学机器人研究所, 天津 300300

收稿日期:2015-07-24 修回日期:2015-09-06 出版日期:2016-01-10 发布日期:2016-01-09
通讯作者: 李永胜(1989-),男,山东菏泽人,硕士研究生,主要研究方向:智能优化算法
作者简介:高庆吉(1966-),男,黑龙江桦川人,教授,博士,主要研究方向:模式识别、人工智能、机器人导航控制;罗其俊(1982-),男,湖北孝感人,讲师,硕士研究生,主要研究方向:数据挖掘、图像处理。
基金资助:
天津市自然科学基金重点支持项目(12JCZDJC34200)。

Optimization of baggage tag reader layout based on improved particle swarm optimization

GAO Qingji, LI Yongsheng, LUO Qijun

Robotics Institute, Civil Aviation University of China, Tianjin 300300, China

Received:2015-07-24 Revised:2015-09-06 Online:2016-01-10 Published:2016-01-09
Supported by:
This work is partially supported by the Key Project of Natural Science Foundation of Tianjin (12JCZDJC34200).

摘要/Abstract

摘要： 针对航空旅客托运行李时,检测行李条码的阅读器数量、位置、姿态存在很多不确定性问题,提出了动态种群-双适应值粒子群优化(DPDF-PSO)算法。首先,建立行李条码检测数学模型;然后,转化为约束优化问题;其次,通过标准粒子群优化(PSO)算法求解此优化问题;最后,依照模型特点对标准粒子群算法进行改进。仿真结果表明,与标准PSO算法相比,DPDF-PSO算法仿真时间降低了23.6%,目标函数值提高了3.7%。DPDF-PSO算法克服了标准粒子群优化算法中仿真时间慢、边界最优解难处理的缺点,阅读器布局方案能以较低的成本准确快速读取行李身份信息。

关键词: 条码检测, 建模, 粒子群优化, 动态种群, 双适应值

Abstract: When civil aviation passengers check in, various uncertainty problems exist in the baggage tag readers' number, position and angle. To solve the problems, the Dynamic Population-Double Fitness Particle Swarm Optimization (DPDF-PSO) algorithm was proposed. Firstly, the mathematical model of baggage tag detector was established, then it was transformed into an optimization problem; secondly, the optimization problem was solved by standard Particle Swarm Optimization (PSO) algorithm; finally, the standard PSO algorithm was improved in accordance with the model features. The simulation results show that compared with standard PSO algorithm, the simulation time of the DPDF-PSO algorithm reduced by 23.6%, the objective function value increased by 3.7%. DPDF-PSO algorithm overcomes the shortage of long simulation time and troublesome problem of optimal boundary solutions existed in standard PSO algorithm. Identity information can be read quickly and accurately by readers layout at a lower cost.

Key words: tag detection, modeling, Particle Swarm Optimization (PSO), dynamic population, double fitness value

中图分类号:

高庆吉, 李永胜, 罗其俊. 基于改进粒子群算法的行李条码阅读器布局优化[J]. 计算机应用, 2016, 36(1): 128-132.

GAO Qingji, LI Yongsheng, LUO Qijun. Optimization of baggage tag reader layout based on improved particle swarm optimization[J]. Journal of Computer Applications, 2016, 36(1): 128-132.

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基于改进粒子群算法的行李条码阅读器布局优化

Optimization of baggage tag reader layout based on improved particle swarm optimization

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