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

Muhammad Shafiq; Zain Anwar Ali; Amber Israr; Eman H Alkhammash; Myriam Hadjouni; Jari Juhani Jussila

doi:10.3390/s22145395

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

Sensors (Basel). 2022 Jul 19;22(14):5395. doi: 10.3390/s22145395.

Authors

Muhammad Shafiq¹, Zain Anwar Ali¹, Amber Israr¹, Eman H Alkhammash², Myriam Hadjouni³, Jari Juhani Jussila⁴

Affiliations

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.

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: Cauchy mutation; Max-Min Ant Colony Optimization; differential evolution; path planning.

MeSH terms

Algorithms*
Computer Simulation

Grants and funding

The authors would like to acknowledge Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R193), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, and the European Regional Development Project Green Smart Services in Developing Circular Economy SMEs (A77472).