Clustering Methods for Agent Distribution Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00168913" target="_blank" >RIV/68407700:21230/10:00168913 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Clustering Methods for Agent Distribution Optimization

Popis výsledku v původním jazyce

Multiagent systems consist of a collection of agents that directly interact usually via a form of message passing. Information about these interactions can be analyzed in an online or offline way to identify clusters of agents that are related. The firstpart of this paper is dedicated to a formal definition of a proposed dynamic model for agent clustering. The main contribution is the ability to discover and visualize communication neighborhoods of agents at runtime. The second part of this paper dealswith a static agent clustering problem where equally sized clusters with maximal intracluster communication among agents are sought in order to efficiently distribute agents across multiple execution units. A multiobjective clustering approach based onan iterative multiobjective optimization evolutionary algorithm is proposed and its advantages are demonstrated.

Název v anglickém jazyce

Clustering Methods for Agent Distribution Optimization

Popis výsledku anglicky

Multiagent systems consist of a collection of agents that directly interact usually via a form of message passing. Information about these interactions can be analyzed in an online or offline way to identify clusters of agents that are related. The firstpart of this paper is dedicated to a formal definition of a proposed dynamic model for agent clustering. The main contribution is the ability to discover and visualize communication neighborhoods of agents at runtime. The second part of this paper dealswith a static agent clustering problem where equally sized clusters with maximal intracluster communication among agents are sought in order to efficiently distribute agents across multiple execution units. A multiobjective clustering approach based onan iterative multiobjective optimization evolutionary algorithm is proposed and its advantages are demonstrated.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Systems, Man, and Cybernetics: Part C

ISSN

1094-6977

e-ISSN

—

Svazek periodika

40

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

000271605100007

EID výsledku v databázi Scopus

—