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Volume 22
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10.3390/s22145395
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Article

Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach

by
Muhammad Shafiq
1,
Zain Anwar Ali
1,*,
Amber Israr
1,
Eman H. Alkhammash
2,
Myriam Hadjouni
3 and
Jari Juhani Jussila
4
1
Electronic Engineering Department, Sir Syed University of Engineering & Technology, Karachi 75300, Pakistan
2
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
3
Department of Computer Sciences, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4
HAMK Design Factory, Häme University of Applied Sciences, 13100 Hämeenlinna, Finland
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(14), 5395; https://doi.org/10.3390/s22145395
Submission received: 24 May 2022 / Revised: 29 June 2022 / Accepted: 18 July 2022 / Published: 19 July 2022
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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Abstract

Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.
Keywords: path planning; Max-Min Ant Colony Optimization; differential evolution; Cauchy mutation path planning; Max-Min Ant Colony Optimization; differential evolution; Cauchy mutation

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MDPI and ACS Style

Shafiq, M.; Ali, Z.A.; Israr, A.; Alkhammash, E.H.; Hadjouni, M.; Jussila, J.J. Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach. Sensors 2022, 22, 5395. https://doi.org/10.3390/s22145395

AMA Style

Shafiq M, Ali ZA, Israr A, Alkhammash EH, Hadjouni M, Jussila JJ. Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach. Sensors. 2022; 22(14):5395. https://doi.org/10.3390/s22145395

Chicago/Turabian Style

Shafiq, Muhammad, Zain Anwar Ali, Amber Israr, Eman H. Alkhammash, Myriam Hadjouni, and Jari Juhani Jussila. 2022. "Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach" Sensors 22, no. 14: 5395. https://doi.org/10.3390/s22145395

APA Style

Shafiq, M., Ali, Z. A., Israr, A., Alkhammash, E. H., Hadjouni, M., & Jussila, J. J. (2022). Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach. Sensors, 22(14), 5395. https://doi.org/10.3390/s22145395

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