Giuseppe Converso , Duaa Mehiar , Alexander Rukovich , Rashit Brzhanov
{"title":"Report on optimisation for efficient dynamic task distribution in drone swarms using QRDPSO algorithm","authors":"Giuseppe Converso , Duaa Mehiar , Alexander Rukovich , Rashit Brzhanov","doi":"10.1016/j.asej.2024.103237","DOIUrl":null,"url":null,"abstract":"<div><div>The primary aim was to develop a Quantum Robot Darwinian Particle Swarm Optimisation (QRDPSO) algorithm and assess its performance against the conventional RDPSO. Using MATLAB-based mathematical modelling, QRDPSO was evaluated for its efficiency in dynamic task distribution and inter-drone communication stability. The results demonstrate that QRDPSO finds optimal solutions 16.3% faster than RDPSO, with performance improvements as the swarm size increases. Specifically, when the number of drones was increased from 5 to 20, the number of iterations required for QRDPSO changed from 384 to 189. However, for RDPSO, the number of iterations changed from 439 to 242. Additionally, QRDPSO showed a 27.1% reduction in drone loss rates, outperforming RDPSO in terms of maintaining operational resources, especially in larger swarms. These findings have practical implications, as QRDPSO’s efficiency and stability can support extensive drone applications requiring synchronised, reliable swarm behaviour.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103237"},"PeriodicalIF":6.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S209044792400618X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The primary aim was to develop a Quantum Robot Darwinian Particle Swarm Optimisation (QRDPSO) algorithm and assess its performance against the conventional RDPSO. Using MATLAB-based mathematical modelling, QRDPSO was evaluated for its efficiency in dynamic task distribution and inter-drone communication stability. The results demonstrate that QRDPSO finds optimal solutions 16.3% faster than RDPSO, with performance improvements as the swarm size increases. Specifically, when the number of drones was increased from 5 to 20, the number of iterations required for QRDPSO changed from 384 to 189. However, for RDPSO, the number of iterations changed from 439 to 242. Additionally, QRDPSO showed a 27.1% reduction in drone loss rates, outperforming RDPSO in terms of maintaining operational resources, especially in larger swarms. These findings have practical implications, as QRDPSO’s efficiency and stability can support extensive drone applications requiring synchronised, reliable swarm behaviour.
期刊介绍:
in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance.
Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.