QSACO: A QoS-Based Self-Adapted Ant Colony Optimization

Abstract

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 high-computing-acquirement problems of QSACO, the proposed method could be used in the mobile cloud environment.