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14 pages, 3775 KiB

Open AccessArticle

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

by Muhammad Shafiq, Zain Anwar Ali, Amber Israr, Eman H. Alkhammash, Myriam Hadjouni and Jari Juhani Jussila

Sensors 2022, 22(14), 5395; https://doi.org/10.3390/s22145395 - 19 Jul 2022

Cited by 24 | Viewed by 3422

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. Full article

(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)

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15 pages, 13243 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs

by Muhammad Shafiq, Zain Anwar Ali, Amber Israr, Eman H. Alkhammash and Myriam Hadjouni

Drones 2022, 6(5), 104; https://doi.org/10.3390/drones6050104 - 23 Apr 2022

Cited by 16 | Viewed by 3430

Abstract

This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimization (MMACO) in conjunction with social learning mechanism to plan the optimized path for an individual colony. Hereinafter, the multi-agent system (MAS) chooses the most optimal UAV as the leader of each colony and the remaining UAVs as agents, which helps to organize the randomly positioned UAVs into three different formations. Afterward, the algorithm synchronizes and connects the three colonies into a swarm and controls it using dynamic leader selection. The major contribution of this study is to hybridize two different approaches to produce a more optimized, efficient, and effective strategy. The results verify that the proposed algorithm completes the given objectives. This study also compares the designed method with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to prove that our method offers better convergence and reaches the target using a shorter route than NSGA-II. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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15 pages, 5754 KiB

Open AccessArticle

A Cluster-Based Hierarchical-Approach for the Path Planning of Swarm

by Muhammad Shafiq, Zain Anwar Ali and Eman H. Alkhammash

Appl. Sci. 2021, 11(15), 6864; https://doi.org/10.3390/app11156864 - 26 Jul 2021

Cited by 21 | Viewed by 2748

Abstract

This paper addresses the path planning and control of multiple colonies/clusters that have unmanned aerial vehicles (UAV) which make a network in a hazardous environment. To solve the aforementioned issue, we design a new and novel hybrid algorithm. As seen in the mission requirement, to combine the Maximum-Minimum ant colony optimization (ACO) with Vicsek based multi-agent system (MAS) to make an Artificially Intelligent (AI) scheme. In order to control and manage the different colonies, UAVs make a form of a network. The designed method overcomes the deficiencies of existing algorithms related to controlling and synchronizing the information globally. Furthermore, our designed architecture bounds, lemmatizes the pheromone, and finds the best ants which then make the most optimized path. The key contribution of this study is to merge two unique algorithms into a hybrid algorithm that has superior performance than both algorithms operating separately. Another contribution of the designed method is the ability to increase the number of individual agents inside the colony or the number of colonies with a good convergence rate. Lastly, we also compared the simulation results with the non-dominated sorting genetic algorithm II (NSGA-II) in order to prove the designed algorithm has a better convergence rate. Full article

(This article belongs to the Special Issue Intelligent Control in Industrial and Renewable Systems)

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