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Probability collectives: a distributed multi-agent system approach for optimization

This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniqu...

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Detalles Bibliográficos
Autores principales: Kulkarni, Anand Jayant, Tai, Kang, Abraham, Ajith
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-16000-9
http://cds.cern.ch/record/1996675
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author Kulkarni, Anand Jayant
Tai, Kang
Abraham, Ajith
author_facet Kulkarni, Anand Jayant
Tai, Kang
Abraham, Ajith
author_sort Kulkarni, Anand Jayant
collection CERN
description This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
publisher Springer
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spelling cern-19966752021-04-21T20:26:56Zdoi:10.1007/978-3-319-16000-9http://cds.cern.ch/record/1996675engKulkarni, Anand JayantTai, KangAbraham, AjithProbability collectives: a distributed multi-agent system approach for optimizationEngineeringThis book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.Springeroai:cds.cern.ch:19966752015
spellingShingle Engineering
Kulkarni, Anand Jayant
Tai, Kang
Abraham, Ajith
Probability collectives: a distributed multi-agent system approach for optimization
title Probability collectives: a distributed multi-agent system approach for optimization
title_full Probability collectives: a distributed multi-agent system approach for optimization
title_fullStr Probability collectives: a distributed multi-agent system approach for optimization
title_full_unstemmed Probability collectives: a distributed multi-agent system approach for optimization
title_short Probability collectives: a distributed multi-agent system approach for optimization
title_sort probability collectives: a distributed multi-agent system approach for optimization
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-16000-9
http://cds.cern.ch/record/1996675
work_keys_str_mv AT kulkarnianandjayant probabilitycollectivesadistributedmultiagentsystemapproachforoptimization
AT taikang probabilitycollectivesadistributedmultiagentsystemapproachforoptimization
AT abrahamajith probabilitycollectivesadistributedmultiagentsystemapproachforoptimization