Autonomous addition of agents to an existing group using genetic algorithm

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dc.contributor.author Mondal, Sabyasachi
dc.contributor.author Tsourdos, Antonios
dc.date.accessioned 2021-02-19T12:54:29Z
dc.date.available 2021-02-19T12:54:29Z
dc.date.issued 2020-12-05
dc.identifier.citation Mondal S, Tsourdos A. (2020) Autonomous addition of agents to an existing group using genetic algorithm. Sensors, Volume 20, Issue 23, 2020, Article number 6953 en_UK
dc.identifier.issn 1424-8220
dc.identifier.uri https://doi.org/10.3390/s20236953
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/16379
dc.description.abstract 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 en_UK
dc.language.iso en en_UK
dc.publisher MDPI en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject BVLOS en_UK
dc.subject autonomous mission en_UK
dc.subject two-dimensional genetic algorithm en_UK
dc.subject optimal topology en_UK
dc.subject distributed control en_UK
dc.title Autonomous addition of agents to an existing group using genetic algorithm en_UK
dc.type Article en_UK


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