Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle
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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.
KeywordsDynamic Adaptive Ant Lion Optimizer (DAALO) Route planning Unmanned aerial vehicle (UAV) Ant Lion Optimizer (ALO) Levy flight 1/5 Principle
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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Conflict of interest
The authors declare that they do not have any conflicts of interest to this work.
- Bollino K, Lewis LR, Sekhavat P, Ross IM, (2007) Pseudospectral Optimal Control: A Clear Road for Autonomous Intelligent Path Planning. In: AIAA Infotech@Aerospace, (2007) Conference and Exhibit. Rohnert Park, California, USAGoogle Scholar
- de la Cruz JM, Besada-Portas E, de la Torre L, de Andres-Toro B, Lopez-Orozco JA (2008) Evolutionary path planner for UAVs in realistic environments. Genetic and Evolutionary Computation Conference. Atlanta, Georgia, USA, pp 1447–1484Google Scholar
- Hrabar S (2008) 3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs. Proceedings of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, FRANCE, pp 22–26Google Scholar
- Karaman S, Walter MR, Perez A (2011) Anytime motion planning using the RRT*. Proceedings of IEEE International Conference Robotics and Automation. Shanghai, China, pp 1478–1483Google Scholar
- Ma CS, Miller RH (2006) MILP optimal path planning for real-time applications. Proceedings of the American Control Conference. Minneapolis, MN, pp 4945–4950Google Scholar
- Oz I, Topcuoglu HR, Ermis M (2013) A meta-heuristic based three-dimensional path planning environment for unmanned aerial vehicles. Simul-T Soc Mod Sim 89(8):903–920Google Scholar
- Vera S, Cobano JA, Heredia G, Ollero A (2014) An hp-adaptative pseudospectral method for collision avoidance with multiple UAVs in real-time applications. IEEE International Conference on Robotics & Automation. Hongkong, China, pp 4717–4722Google Scholar
- Wang HL, Lyu WT, Yao P,liang X, Liu C,(2015) Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system. Chinese J Aeronaut 28(1):229–239Google Scholar
- Yang XS (2011) Nature-inspired metaheuristic algorithms. Luniver Press, United KingdomGoogle Scholar
- Yershov DS, Lavalle SM (2011) Simplicial Dijkstra and A* algorithms for optimal feedback planning. Proceedings of 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. CA, USA, San Francisco, pp 3862–3867Google Scholar
- Zhang SY, Yu JQ, Sun HD (2015) UAV path planning via Legendre pseudospectral method improved by differential flatness. the 27th Chinese Control and Decision Conference. Qingdao, China, pp 2580–2584Google Scholar