兰州理工大学学报

兰州理工大学学报 ›› 2020, Vol. 46 ›› Issue (1): 106-110.

• 自动化技术与计算机技术 • 上一篇    下一篇

基于GDBA算法目标跟踪的粒子多样性研究

杜先君1,2,3, 马金斗1   

  1. 1.兰州理工大学 电气工程与信息工程学院, 甘肃 兰州 730050;
    2.兰州理工大学 甘肃省工业过程先进控制重点实验室, 甘肃 兰州 730050;
    3.兰州理工大学 电气与控制工程国家实验教学示范中心, 甘肃 兰州 730050
  • 收稿日期:2018-07-02 出版日期:2020-02-28 发布日期:2020-06-23
  • 作者简介:杜先君(1979-),男,浙江杭州人,博士,副教授.

Investigation of particle variety based on target tracking of GDBA algorithm

DU Xian-jun1,2,3, MA Jin-dou1   

  1. 1. College of Electrical and Information Engineering, Lanzhou Univ. of Tech. , Lanzhou 730050, China;
    2. Key Laboratory of Gansu Advanced Control for Industrial Process, Lanzhou Univ. of Tech. , Lanzhou 730050, China;
    3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou 730050, China
  • Received:2018-07-02 Online:2020-02-28 Published:2020-06-23

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摘要： 针对传统目标跟踪算法搜索范围小、跟踪精度低的缺点,提出一种基于遗传扰动机制的改进蝙蝠算法(GDBA),该算法引入了遗传竞争机制,根据优化的优劣情况调整遗传算法的交叉率和变异率,使得种群具有遗传性和变异性,同时扩大了搜索范围,提高了粒子多样性,改善了跟踪精度.

关键词: 竞争机制, 跟踪精度, GDBA算法, 粒子多样性

Abstract: Aimed at the defect of small searching scope and low tracking accuracy of traditional target tracking algorithm, an improved bat algorithm(GDBA) is proposed based on genetic disturbance mechanism. In this algorithm, the genetic competitive mechanism is introduced to improve the bat algorithm, the crossover factor and the mutation rate in the genetic algorithm are adjusted according to the good-bad condition of optimization, so that the population will be made to have heritability and diversity and meantime, the searching range will be expanded and the tracking accuracy improved.

Key words: competition mechanism, tracking accuracy, GDBA algorithm, particle variety

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