A Genetic Approach for Long Term Virtual Organization Distribution

Victor Sanchez-Anguix, Soledad Valero, Ana Garcia-Fornes

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.


Original languageEnglish
Pages (from-to)271-295
Number of pages25
JournalInternational Journal on Artificial Intelligence Tools
Volume20
Issue number2
DOIs
Publication statusPublished - 2011

Fingerprint

Genetic algorithms
Multi agent systems
Resource allocation

Keywords

  • Virtual organizations
  • genetic algorithms
  • multi-agent systems

Cite this

A Genetic Approach for Long Term Virtual Organization Distribution. / Sanchez-Anguix, Victor; Valero, Soledad; Garcia-Fornes, Ana.

In: International Journal on Artificial Intelligence Tools, Vol. 20, No. 2, 2011, p. 271-295.

Research output: Contribution to journalArticle

Sanchez-Anguix, Victor ; Valero, Soledad ; Garcia-Fornes, Ana. / A Genetic Approach for Long Term Virtual Organization Distribution. In: International Journal on Artificial Intelligence Tools. 2011 ; Vol. 20, No. 2. pp. 271-295.
@article{1c733f7d4eab4aab932f14a1bfb8dd5f,
title = "A Genetic Approach for Long Term Virtual Organization Distribution",
abstract = "An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.",
keywords = "Virtual organizations, genetic algorithms, multi-agent systems",
author = "Victor Sanchez-Anguix and Soledad Valero and Ana Garcia-Fornes",
year = "2011",
doi = "10.1142/S0218213011000152",
language = "English",
volume = "20",
pages = "271--295",
journal = "International Journal on Artificial Intelligence Tools",
issn = "0218-2130",
publisher = "World Scientific",
number = "2",

}

TY - JOUR

T1 - A Genetic Approach for Long Term Virtual Organization Distribution

AU - Sanchez-Anguix, Victor

AU - Valero, Soledad

AU - Garcia-Fornes, Ana

PY - 2011

Y1 - 2011

N2 - An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

AB - An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

KW - Virtual organizations

KW - genetic algorithms

KW - multi-agent systems

U2 - 10.1142/S0218213011000152

DO - 10.1142/S0218213011000152

M3 - Article

VL - 20

SP - 271

EP - 295

JO - International Journal on Artificial Intelligence Tools

JF - International Journal on Artificial Intelligence Tools

SN - 0218-2130

IS - 2

ER -