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Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical be...

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Detalles Bibliográficos
Autores principales: Mondal, Sabyasachi, Tsourdos, Antonios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730348/
https://www.ncbi.nlm.nih.gov/pubmed/33291396
http://dx.doi.org/10.3390/s20236953
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author Mondal, Sabyasachi
Tsourdos, Antonios
author_facet Mondal, Sabyasachi
Tsourdos, Antonios
author_sort Mondal, Sabyasachi
collection PubMed
description This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing.
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spelling pubmed-77303482020-12-12 Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm Mondal, Sabyasachi Tsourdos, Antonios Sensors (Basel) Article This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing. MDPI 2020-12-05 /pmc/articles/PMC7730348/ /pubmed/33291396 http://dx.doi.org/10.3390/s20236953 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mondal, Sabyasachi
Tsourdos, Antonios
Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
title Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
title_full Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
title_fullStr Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
title_full_unstemmed Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
title_short Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm
title_sort autonomous addition of agents to an existing group using genetic algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730348/
https://www.ncbi.nlm.nih.gov/pubmed/33291396
http://dx.doi.org/10.3390/s20236953
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