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Optimizing UAV Path for Disaster Management in Smart Cities Using Metaheuristic Algorithms

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Computational Intelligence for Unmanned Aerial Vehicles Communication Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1033))

Abstract

In this research, different state-of-the-art metaheuristic algorithms are examined in order to incorporate a collision-free path planning technique for Unmanned Air Vehicles (UAV) in crisis situations. The effective path planning is utilized to identify the major cause of disasters such as bushfires, which are wreaking havoc on the global forest environment. This new approach is a first step toward a pre-disaster evaluation and options for saving survivors in a shorter amount of time. For UAV path optimization, a novel meta-heuristic algorithm i.e. Smart Flower Optimization Algorithm (SFOA) is presented. Comparison is made between different metaheuristic algorithms such as Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA) and Grasshopper Optimization (GHO). Four different scenarios are offered to demonstrate the resilience of our proposed model that are dynamic environment (DE), general environment (GE), condensed environment (CE) and maze environment (ME). The barriers in each scenario are put in such a way that the overall path complexity for a UAV to reach the destination is increased. In SFOA, two growth methods are controlled on the movement of immature flower and mathematical modelling is used to update particles position. The SFOA method beats other algorithms based on the parameters chosen, saving up to 24.5% in transportation costs and 13.3% in computational time. As a result, SFOA can be used to optimize UAV path planning in any of the foregoing environment.

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Author information

Authors and Affiliations

  1. School of Engineering, Design and Built Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia

    Zakria Qadir, Khoa N. Le & Vivian W. Y. Tam

  2. Capital University of Science and Technology, Islamabad, 44000, ISB, Pakistan

    Muhammad Hamza Zafar

  3. National University of Sciences and Technology, Islamabad, 44000, ISB, Pakistan

    Syed Kumayl Raza Moosavi

Authors
  1. Zakria Qadir
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  2. Muhammad Hamza Zafar
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  3. Syed Kumayl Raza Moosavi
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  4. Khoa N. Le
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  5. Vivian W. Y. Tam
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Corresponding author

Correspondence to Zakria Qadir .

Editor information

Editors and Affiliations

  1. Universite Moulay Ismail De Meknes, Meknes, Morocco

    Mariya Ouaissa

  2. Isra University, Islamabad, Pakistan

    Inam Ullah Khan

  3. Moulay Ismail University, Meknes, Morocco

    Mariyam Ouaissa

  4. Mohammedia, Faculte des Sciences et Techniques, El Mansouria, Morocco

    Zakaria Boulouard

  5. Dalian University of Technology, Dalian, China

    Syed Bilal Hussain Shah

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Qadir, Z., Zafar, M.H., Moosavi, S.K.R., Le, K.N., Tam, V.W.Y. (2022). Optimizing UAV Path for Disaster Management in Smart Cities Using Metaheuristic Algorithms. In: Ouaissa, M., Khan, I.U., Ouaissa, M., Boulouard, Z., Hussain Shah, S.B. (eds) Computational Intelligence for Unmanned Aerial Vehicles Communication Networks. Studies in Computational Intelligence, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-97113-7_13

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