Modelling Shortest Path Search Techniques by Colonies of Cooperating Agents

Authors:
Dariusz Król;Łukasz Popiela
Affiliations:
Institute of Informatics, Wrocław University of Technology, Wrocław, Poland 50-370;Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland 50-371
Venue:
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Year:
2009

Citing 8
Cited 1

Multi-agent coordination and control using stigmergy

Computers in Industry
A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks

Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
Performance analysis of the AntNet algorithm

Computer Networks: The International Journal of Computer and Telecommunications Networking
Swarms and Swarm Intelligence

Computer
Ant colony intelligence in multi-agent dynamic manufacturing scheduling

Engineering Applications of Artificial Intelligence
Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment

Ad Hoc Networks
Review: A review of ant algorithms

Expert Systems with Applications: An International Journal
Ant system: optimization by a colony of cooperating agents

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

Swarm-based multi-agent simulation: a case study of urban traffic flow in the city of wroclaw

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a nature-inspired agent coordination model for comparing ants', bees' and beents' behaviour in finding the shortest path when searching for food. The model is based on Jadex. Simulations demonstrate that the system can be trained to solve a wide range of problems efficiently. The model also provides means for estimating and interpreting common errors of these multi-agents simulations.