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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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2015
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-16000-9 http://cds.cern.ch/record/1996675 |
_version_ | 1780945880902270976 |
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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. |
id | cern-1996675 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2015 |
publisher | Springer |
record_format | invenio |
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 |