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

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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.

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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).

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Peng Yao & Honglun Wang

  2. Unmanned Aerial Vehicle Research Institute, Beihang University, Beijing, 100191, China

    Peng Yao & Honglun Wang

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  1. Peng Yao
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  2. Honglun Wang
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Correspondence to Honglun Wang.

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

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Communicated by V. Loia.

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Yao, P., Wang, H. Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle. Soft Comput 21, 5475–5488 (2017). https://doi.org/10.1007/s00500-016-2138-6

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