QSACO: A QoS-based Self-adapted Ant Colony Optimization

被引：1

作者：

Sun, Weifeng ^{[1
]}

Xing, Yuanxun ^{[1
]}

Zhou, Chi ^{[1
]}

Zhang, Shenwei ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Software Technol, Dalian 116620, Peoples R China

来源：

2017 5TH IEEE INTERNATIONAL CONFERENCE ON MOBILE CLOUD COMPUTING, SERVICES, AND ENGINEERING (MOBILECLOUD) | 2017年

基金：

美国国家科学基金会;

关键词：

multi-UAV system; self-adaptive; QSACO; mobile cloud;

D O I：

10.1109/MobileCloud.2017.25

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Unmanned aerial vehicles have some characteristics such as strong flexibility and lower costs that are suitable for capturing information in special scenarios and environments. Collaborative working of multi-UAV system is an important performance metric for mobile computing in wireless networks. Ant Colony Algorithm is a dynamic path selecting optimization algorithm and it can be used in multi-UAV system to adapt dynamic situations. An improved ACO based on PSO algorithm called QSACO is proposed to dynamically adjust the parameters of ACO and to ensure the users' QoS demands. To solve the highcomputing- acquirement problems of QSACO, the proposed method could be used in the mobile cloud environment.

引用

页码：157 / 160

页数：4

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