Soft Computing

, Volume 21, Issue 18, pp 5475–5488 | Cite as

Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle

Methodologies and Application

Abstract

This paper proposes a novel Dynamic Adaptive Ant Lion Optimizer (DAALO) for route planning of unmanned aerial vehicle. Ant Lion Optimizer (ALO) is a new intelligent algorithm motivated by the phenomenon that antlions hunt ants in nature, showing the great potential to solve the optimization problems of engineering. In the proposed DAALO, the random walk of ants is replaced by Levy flight to make ALO escape from local optima more easily. Besides, by introducing the improvement rate of population as the feedback, the size of trap is adjusted dynamically based on the 1/5 Principle to improve the performance of ALO including convergence accuracy, convergence speed and stability. Compared to some other bio-inspired methods, the proposed algorithm are utilized to find the optimal route in two different environments such as mountain model and city model. The comparison results demonstrate the effectiveness, robustness and feasibility of DAALO.

Keywords

Dynamic Adaptive Ant Lion Optimizer (DAALO) Route planning Unmanned aerial vehicle (UAV) Ant Lion Optimizer (ALO) Levy flight 1/5 Principle 

Notes

Acknowledgments

This study was supported by the National Natural Science Foundation of China (No. 61175084) and Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13004).

Compliance with ethical standards

Conflict of interest

The authors declare that they do not have any conflicts of interest to this work.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Unmanned Aerial Vehicle Research InstituteBeihang UniversityBeijingChina

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